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  <front>
    <journal-meta>
      <journal-id journal-id-type="nlm-ta">Arq Bras Cardiol</journal-id>
      <journal-id journal-id-type="publisher-id">abc</journal-id>
      <journal-title-group>
        <journal-title>Arquivos Brasileiros de Cardiologia</journal-title>
        <abbrev-journal-title abbrev-type="publisher">Arq. Bras. Cardiol.</abbrev-journal-title>
      </journal-title-group>
      <issn pub-type="ppub">0066-782X</issn>
      <issn pub-type="epub">1678-4170</issn>
      <publisher>
        <publisher-name>Sociedade Brasileira de Cardiologia - SBC</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id specific-use="scielo-v3" pub-id-type="publisher-id">zgdwFYkR9LkvL5Bdz8LWjmj</article-id>
      <article-id specific-use="scielo-v2" pub-id-type="publisher-id">S0066-782X2026000600607</article-id>
      <article-id pub-id-type="other">00607</article-id>
      <article-id pub-id-type="doi">10.36660/abc.20260064</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Artigo Original</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>An&#225;lise T&#234;mporo-Espacial da Mortalidade por Cardiomiopatias no Brasil entre 2001 e 2021</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0000-0002-6546-9841</contrib-id>
          <name>
            <surname>Kubrusly</surname>
            <given-names>Luiz Fernando</given-names>
          </name>
          <role>Concep&#231;&#227;o e desenho da pesquisa</role>
          <role>Obten&#231;&#227;o de dados</role>
          <role>An&#225;lise e interpreta&#231;&#227;o dos dados</role>
          <role>Reda&#231;&#227;o do manuscrito</role>
          <role>Revis&#227;o cr&#237;tica do manuscrito quanto ao conte&#250;do intelectual importante</role>
          <xref ref-type="aff" rid="aff1">
            <sup>1</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0009-0005-9233-4888</contrib-id>
          <name>
            <surname>Lopes</surname>
            <given-names>Ana Clara Abdo</given-names>
          </name>
          <role>Concep&#231;&#227;o e desenho da pesquisa</role>
          <role>Reda&#231;&#227;o do manuscrito</role>
          <role>Revis&#227;o cr&#237;tica do manuscrito quanto ao conte&#250;do intelectual importante</role>
          <xref ref-type="aff" rid="aff1">
            <sup>1</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0009-0006-5196-4426</contrib-id>
          <name>
            <surname>Souza</surname>
            <given-names>Fernanda Ritt de</given-names>
          </name>
          <role>Concep&#231;&#227;o e desenho da pesquisa</role>
          <role>Obten&#231;&#227;o de dados</role>
          <role>An&#225;lise e interpreta&#231;&#227;o dos dados</role>
          <role>Reda&#231;&#227;o do manuscrito</role>
          <role>Revis&#227;o cr&#237;tica do manuscrito quanto ao conte&#250;do intelectual importante</role>
          <xref ref-type="aff" rid="aff1">
            <sup>1</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0009-0000-5872-3867</contrib-id>
          <name>
            <surname>Dieter</surname>
            <given-names>Gustavo Ferrucio</given-names>
          </name>
          <role>Concep&#231;&#227;o e desenho da pesquisa</role>
          <role>Obten&#231;&#227;o de dados</role>
          <role>An&#225;lise e interpreta&#231;&#227;o dos dados</role>
          <role>Reda&#231;&#227;o do manuscrito</role>
          <role>Revis&#227;o cr&#237;tica do manuscrito quanto ao conte&#250;do intelectual importante</role>
          <xref ref-type="aff" rid="aff1">
            <sup>1</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0009-0003-2757-7862</contrib-id>
          <name>
            <surname>Nadolny</surname>
            <given-names>Laura</given-names>
          </name>
          <role>Concep&#231;&#227;o e desenho da pesquisa</role>
          <role>An&#225;lise e interpreta&#231;&#227;o dos dados</role>
          <role>Reda&#231;&#227;o do manuscrito</role>
          <role>Revis&#227;o cr&#237;tica do manuscrito quanto ao conte&#250;do intelectual importante</role>
          <xref ref-type="aff" rid="aff1">
            <sup>1</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0009-0000-6644-6177</contrib-id>
          <name>
            <surname>Dias</surname>
            <given-names>Leonardo Moreira</given-names>
          </name>
          <role>Concep&#231;&#227;o e desenho da pesquisa</role>
          <role>Obten&#231;&#227;o de dados</role>
          <role>An&#225;lise e interpreta&#231;&#227;o dos dados</role>
          <role>An&#225;lise estat&#237;stica</role>
          <role>Revis&#227;o cr&#237;tica do manuscrito quanto ao conte&#250;do intelectual importante</role>
          <xref ref-type="aff" rid="aff1">
            <sup>1</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0009-0000-1954-3994</contrib-id>
          <name>
            <surname>Dias</surname>
            <given-names>Let&#237;cia Moreira</given-names>
          </name>
          <role>Concep&#231;&#227;o e desenho da pesquisa</role>
          <role>Obten&#231;&#227;o de dados</role>
          <role>An&#225;lise e interpreta&#231;&#227;o dos dados</role>
          <role>Reda&#231;&#227;o do manuscrito</role>
          <role>Revis&#227;o cr&#237;tica do manuscrito quanto ao conte&#250;do intelectual importante</role>
          <xref ref-type="aff" rid="aff2">
            <sup>2</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0009-0003-6980-2159</contrib-id>
          <name>
            <surname>Farias</surname>
            <given-names>Luis Gustavo Machado</given-names>
          </name>
          <role>Concep&#231;&#227;o e desenho da pesquisa</role>
          <role>Obten&#231;&#227;o de dados</role>
          <role>An&#225;lise e interpreta&#231;&#227;o dos dados</role>
          <role>An&#225;lise estat&#237;stica</role>
          <role>Reda&#231;&#227;o do manuscrito</role>
          <xref ref-type="aff" rid="aff1">
            <sup>1</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0000-0002-2114-9184</contrib-id>
          <name>
            <surname>Basso</surname>
            <given-names>Roberto Oliveira</given-names>
          </name>
          <role>Concep&#231;&#227;o e desenho da pesquisa</role>
          <role>Obten&#231;&#227;o de dados</role>
          <role>An&#225;lise e interpreta&#231;&#227;o dos dados</role>
          <role>Reda&#231;&#227;o do manuscrito</role>
          <role>Revis&#227;o cr&#237;tica do manuscrito quanto ao conte&#250;do intelectual importante</role>
          <xref ref-type="aff" rid="aff1">
            <sup>1</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0000-0001-9872-9120</contrib-id>
          <name>
            <surname>Okimura</surname>
            <given-names>Vit&#243;ria Naomi</given-names>
          </name>
          <role>Concep&#231;&#227;o e desenho da pesquisa</role>
          <role>Obten&#231;&#227;o de dados</role>
          <role>An&#225;lise e interpreta&#231;&#227;o dos dados</role>
          <role>Reda&#231;&#227;o do manuscrito</role>
          <xref ref-type="aff" rid="aff1">
            <sup>1</sup>
          </xref>
        </contrib>
      </contrib-group>
      <aff id="aff1">
        <label>1</label>
        <institution content-type="orgname">Faculdade Evang&#233;lica Mackenzie do Paran&#225;</institution>
        <addr-line>
          <named-content content-type="city">Curitiba</named-content>
          <named-content content-type="state">PR</named-content>
        </addr-line>
        <country country="BR">Brasil</country>
        <institution content-type="original">Faculdade Evang&#233;lica Mackenzie do Paran&#225;, Curitiba, PR &#8211; Brasil</institution>
      </aff>
      <aff id="aff2">
        <label>2</label>
        <institution content-type="orgname">Faculdade Pequeno Pr&#237;ncipe</institution>
        <addr-line>
          <named-content content-type="city">Curitiba</named-content>
          <named-content content-type="state">PR</named-content>
        </addr-line>
        <country country="BR">Brasil</country>
        <institution content-type="original">Faculdade Pequeno Pr&#237;ncipe, Curitiba, PR &#8211; Brasil</institution>
      </aff>
      <author-notes>
        <corresp id="c01"><label>Correspond&#234;ncia</label>: Ana Clara Abdo Lopes &#8226; Faculdade Evang&#233;lica Mackenzie do Paran&#225; - Rua Padre Anchieta, 2770. CEP 80730-000, Curitiba, PR &#8211; Brasil. E-mail: <email>abdolopesanaclara@gmail.com</email> </corresp>
        <fn fn-type="edited-by">
          <label>Editor respons&#225;vel pela revis&#227;o:</label>
          <p>Nadine Clausell</p>
        </fn>
        <fn fn-type="coi-statement">
          <label>Potencial Conflito de Interesse</label>
          <p> Declaro n&#227;o haver conflito de interesses pertinentes.</p>
        </fn>
      </author-notes>
      <pub-date date-type="pub" publication-format="electronic">
        <day>27</day>
        <month>07</month>
        <year>2026</year>
      </pub-date>
      <pub-date date-type="collection" publication-format="electronic">
        <year>2026</year>
      </pub-date>
      <volume>123</volume>
      <issue>6</issue>
      <elocation-id>e20260064</elocation-id>
      <history>
        <date date-type="received">
          <day>02</day>
          <month>02</month>
          <year>2026</year>
        </date>
        <date date-type="rev-recd">
          <day>29</day>
          <month>04</month>
          <year>2026</year>
        </date>
        <date date-type="accepted">
          <day>13</day>
          <month>05</month>
          <year>2026</year>
        </date>
      </history>
      <permissions>
        <license license-type="open-access" xlink:href="https://creativecommons.org/licenses/by/4.0/" xml:lang="pt">
          <license-p>Este &#233; um artigo de acesso aberto distribu&#237;do sob os termos da licen&#231;a de atribui&#231;&#227;o pelo Creative Commons</license-p>
        </license>
      </permissions>
      <abstract>
        <title>Resumo</title>
        <sec>
          <title>Fundamento</title>
          <p> O Brasil apresenta varia&#231;&#245;es regionais na mortalidade por cardiomiopatias, influenciada por caracter&#237;sticas demogr&#225;ficas, desigualdades socioecon&#244;micas e diferen&#231;as no acesso aos servi&#231;os de sa&#250;de. Entre 2001 e 2021, foram registrados 272.448 &#243;bitos pela doen&#231;a no pa&#237;s.</p>
        </sec>
        <sec>
          <title>Objetivo</title>
          <p> Analisar, em n&#237;vel nacional, o perfil epidemiol&#243;gico e a distribui&#231;&#227;o t&#234;mporo-espacial da mortalidade por cardiomiopatias no Brasil, no per&#237;odo de 2001 a 2021.</p>
        </sec>
        <sec>
          <title>M&#233;todos</title>
          <p> Estudo epidemiol&#243;gico baseado em dados secund&#225;rios do Sistema de Informa&#231;&#227;o sobre Mortalidade (SIM/DATASUS) e estimativas populacionais do IBGE. Calcularam-se taxas brutas de mortalidade por munic&#237;pio. A an&#225;lise temporal foi realizada pelo Joinpoint, estimando-se a varia&#231;&#227;o percentual anual (APC). A an&#225;lise espacial utilizou o GeoDa, com suaviza&#231;&#227;o bayesiana e avalia&#231;&#227;o da autocorrela&#231;&#227;o espacial pelos &#237;ndices de Moran Global e LISA. A signific&#226;ncia estat&#237;stica foi estabelecida em p &lt; 0,05.</p>
        </sec>
        <sec>
          <title>Resultados</title>
          <p> Ocorreram 272.448 &#243;bitos por cardiomiopatias, predominando em homens (58,65%), indiv&#237;duos com 80 anos ou mais (25,74%), brancos (52,18%), casados (36,77%) e com 1 a 3 anos de estudo completos (21,91%). A mortalidade m&#233;dia foi de 6,66 por 100 mil habitantes, atingindo 7,64 por 100 mil em 2004 e caindo para 4,15 por 100 mil em 2020. Observou-se redu&#231;&#227;o anual significativa de 1,86% (p&lt;0,05). A an&#225;lise espacial revelou aglomerados de alta mortalidade principalmente no Centro-Oeste, Sul e Sudeste, com destaque para Corumb&#225; de Goi&#225;s (49,34/100 mil).</p>
        </sec>
        <sec>
          <title>Conclus&#227;o</title>
          <p> Houve redu&#231;&#227;o significativa da mortalidade por cardiomiopatias, embora persistam &#225;reas cr&#237;ticas que exigem estrat&#233;gias direcionadas para vigil&#226;ncia, diagn&#243;stico precoce e manejo adequado.</p>
        </sec>
      </abstract>
      <abstract abstract-type="graphical">
        <p>
          <fig id="f01">
            <label>Figura Central:</label>
            <caption>
              <title>An&#225;lise T&#234;mporo-Espacial da Mortalidade por Cardiomiopatias no Brasil entre 2001 e 2021</title>
            </caption>
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      </abstract>
      <kwd-group xml:lang="pt">
        <title>Palavras-chave:</title>
        <kwd>Cardiomiopatias</kwd>
        <kwd>Mortalidade</kwd>
        <kwd>An&#225;lise Espa&#231;o-Temporal</kwd>
        <kwd>Epidemiologia</kwd>
      </kwd-group>
      <funding-group>
        <funding-statement><bold>Fontes de Financiamento</bold> O presente estudo n&#227;o teve fontes de financiamento externas.</funding-statement>
      </funding-group>
      <counts>
        <fig-count count="12"/>
        <table-count count="2"/>
        <equation-count count="0"/>
        <ref-count count="38"/>
      </counts>
    </article-meta>
  </front>
  <body>
    <sec sec-type="intro">
      <title>Introdu&#231;&#227;o</title>
      <p>As cardiomiopatias constituem um grupo heterog&#234;neo de doen&#231;as do mioc&#225;rdio, caracterizadas por altera&#231;&#245;es estruturais e funcionais que podem evoluir para insufici&#234;ncia card&#237;aca, arritmias malignas e morte s&#250;bita. Segundo a Classifica&#231;&#227;o Internacional de Doen&#231;as (CID-10), essas patologias est&#227;o classificadas sob o c&#243;digo I42. Nas &#250;ltimas d&#233;cadas, observou-se aumento da preval&#234;ncia global dessas doen&#231;as, especialmente da cardiomiopatia dilatada, cuja incid&#234;ncia varia entre 5 e 10 casos por 100.000 habitantes ao ano, refletindo o envelhecimento populacional e a maior carga de doen&#231;as cr&#244;nicas n&#227;o transmiss&#237;veis.<sup><xref ref-type="bibr" rid="B1">1</xref>-<xref ref-type="bibr" rid="B3">3</xref></sup></p>
      <p>A etiologia das cardiomiopatias &#233; multifatorial, envolvendo causas inflamat&#243;rias, t&#243;xicas, metab&#243;licas e gen&#233;ticas, com destaque para muta&#231;&#245;es heredit&#225;rias, particularmente na cardiomiopatia hipertr&#243;fica.<sup><xref ref-type="bibr" rid="B3">3</xref>,<xref ref-type="bibr" rid="B4">4</xref></sup> Fatores de risco como hipertens&#227;o arterial, diabetes mellitus, obesidade e consumo excessivo de &#225;lcool tamb&#233;m est&#227;o associados ao desenvolvimento e &#224; progress&#227;o dessas condi&#231;&#245;es.<sup><xref ref-type="bibr" rid="B5">5</xref></sup></p>
      <p>Clinicamente, os pacientes podem apresentar dispneia, fadiga, palpita&#231;&#245;es e edema perif&#233;rico, com manifesta&#231;&#245;es que variam desde formas assintom&#225;ticas at&#233; quadros graves de insufici&#234;ncia card&#237;aca ou morte s&#250;bita.<sup><xref ref-type="bibr" rid="B6">6</xref>,<xref ref-type="bibr" rid="B7">7</xref></sup> O diagn&#243;stico baseia-se na avalia&#231;&#227;o cl&#237;nica associada a exames complementares, sendo o ecocardiograma considerado o padr&#227;o-ouro para a avalia&#231;&#227;o da fun&#231;&#227;o ventricular e da morfologia card&#237;aca.<sup><xref ref-type="bibr" rid="B8">8</xref></sup> O tratamento &#233; direcionado &#224; etiologia subjacente e inclui terapias farmacol&#243;gicas e interven&#231;&#245;es n&#227;o farmacol&#243;gicas. Apesar dos avan&#231;os terap&#234;uticos, complica&#231;&#245;es e mortalidade permanecem relevantes.<sup><xref ref-type="bibr" rid="B9">9</xref>,<xref ref-type="bibr" rid="B10">10</xref></sup></p>
      <p>No Brasil, estudos evidenciam varia&#231;&#245;es regionais expressivas na mortalidade por cardiomiopatias, sugerindo influ&#234;ncia de fatores demogr&#225;ficos, socioecon&#244;micos e desigualdades no acesso aos servi&#231;os de sa&#250;de.<sup><xref ref-type="bibr" rid="B11">11</xref>-<xref ref-type="bibr" rid="B13">13</xref></sup> Entretanto, grande parte das investiga&#231;&#245;es apresenta recortes temporais limitados ou an&#225;lises regionais isoladas, dificultando a compreens&#227;o integrada da distribui&#231;&#227;o espacial e da tend&#234;ncia temporal da mortalidade em &#226;mbito nacional.<sup><xref ref-type="bibr" rid="B14">14</xref>,<xref ref-type="bibr" rid="B15">15</xref></sup></p>
      <p>Embora estudos pr&#233;vios frequentemente relatem diferen&#231;as regionais utilizando dados agregados, a an&#225;lise espa&#231;o-temporal oferece uma abordagem mais abrangente ao avaliar simultaneamente varia&#231;&#245;es no espa&#231;o e no tempo. Isso permite a identifica&#231;&#227;o de clusters persistentes ou emergentes e uma melhor compreens&#227;o dos padr&#245;es geogr&#225;ficos din&#226;micos.<sup><xref ref-type="bibr" rid="B16">16</xref>,<xref ref-type="bibr" rid="B17">17</xref></sup></p>
      <p>Al&#233;m disso, quest&#245;es metodol&#243;gicas como a agrega&#231;&#227;o espacial (problema da unidade de &#225;rea modific&#225;vel &#8212; MAUP) e o vi&#233;s ecol&#243;gico podem influenciar os resultados, refor&#231;ando a import&#226;ncia de estrat&#233;gias anal&#237;ticas adequadas em epidemiologia espacial.<sup><xref ref-type="bibr" rid="B18">18</xref></sup> T&#233;cnicas de suaviza&#231;&#227;o bayesiana e outros m&#233;todos de estabiliza&#231;&#227;o de taxas tamb&#233;m contribuem para melhorar a confiabilidade das estimativas em pequenas &#225;reas, ao reduzir flutua&#231;&#245;es aleat&#243;rias.<sup><xref ref-type="bibr" rid="B19">19</xref></sup></p>
      <p>Nesse contexto, o presente estudo teve como objetivo analisar o perfil epidemiol&#243;gico e a distribui&#231;&#227;o espa&#231;o-temporal da mortalidade por cardiomiopatias no Brasil entre 2001 e 2021. Como objetivos secund&#225;rios, buscou-se identificar padr&#245;es espaciais e clusters de mortalidade, bem como descrever a distribui&#231;&#227;o dos &#243;bitos segundo caracter&#237;sticas sociodemogr&#225;ficas. Em acr&#233;scimo, o local de ocorr&#234;ncia dos &#243;bitos foi analisado como uma vari&#225;vel descritiva para complementar o perfil epidemiol&#243;gico.</p>
    </sec>
    <sec sec-type="methods">
      <title>M&#233;todos</title>
      <p>Trata-se de um estudo epidemiol&#243;gico de an&#225;lise de dados secund&#225;rios em sa&#250;de com enfoque na an&#225;lise t&#234;mporo-espacial. Os dados referentes aos casos de &#243;bito por cardiomiopatias foram obtidos no Sistema de Informa&#231;&#227;o sobre Mortalidade (SIM), do Departamento de Inform&#225;tica do Sistema &#218;nico de Sa&#250;de (DATASUS) do Minist&#233;rio da Sa&#250;de (MS), via Tabnet, por meio do local de &#243;bito e local de ocorr&#234;ncia. Os dados populacionais utilizados como denominadores para o c&#225;lculo das taxas de mortalidade foram obtidos do DATASUS, com base nas estimativas populacionais do IBGE por munic&#237;pio, sexo e faixa et&#225;ria para o per&#237;odo de 2000 a 2021. Essas estimativas s&#227;o derivadas de m&#233;todos de proje&#231;&#227;o demogr&#225;fica e ajustes intercensit&#225;rios realizados pelo IBGE, utilizando o censo nacional de 2010 como refer&#234;ncia para garantir a consist&#234;ncia da s&#233;rie temporal.</p>
      <p>Para a an&#225;lise da tend&#234;ncia de mortalidade no Brasil, foi selecionado o per&#237;odo de 2001 a 2021, visando melhor compreens&#227;o do desfecho nessa s&#233;rie hist&#243;rica. Trata-se de uma an&#225;lise censit&#225;ria, sem sele&#231;&#227;o individual de participantes, que incluiu a totalidade dos &#243;bitos por cardiomiopatias registrados no SIM entre 2001 e 2021, considerando-se como popula&#231;&#227;o os indiv&#237;duos de todas as idades. Consideraram-se para a an&#225;lise as vari&#225;veis sexo, faixa et&#225;ria, cor/ra&#231;a, estado civil, escolaridade, &#243;bitos por resid&#234;ncia, ano do &#243;bito, local do &#243;bito e local de ocorr&#234;ncia. As categorias de escolaridade seguiram a classifica&#231;&#227;o dispon&#237;vel na base de dados do DATASUS e foram mantidas conforme originalmente fornecidas, a fim de garantir consist&#234;ncia com os sistemas nacionais de informa&#231;&#227;o em sa&#250;de. A vari&#225;vel ra&#231;a/cor foi categorizada conforme a classifica&#231;&#227;o padronizada adotada pelo DATASUS/IBGE.</p>
      <p>Para a causa b&#225;sica desses &#243;bitos, foi adotado o CID I42 da Classifica&#231;&#227;o Internacional de Doen&#231;as (CID-10). As informa&#231;&#245;es do perfil epidemiol&#243;gico e frequ&#234;ncia de &#243;bitos foram compiladas no software Microsoft Excel para Microsoft 365 MSO. As vari&#225;veis nominais foram analisadas por meio da frequ&#234;ncia absoluta e percentual de ocorr&#234;ncia na popula&#231;&#227;o em estudo. A mortalidade anual foi calculada considerando-se o n&#250;mero total de &#243;bitos no estado como numerador e a popula&#231;&#227;o do estado naquele ano como denominador, expressa por 100 mil habitantes. As taxas de mortalidade municipais foram estimadas por meio de uma abordagem baseada na m&#233;dia populacional, definida como o n&#250;mero m&#233;dio de &#243;bitos no per&#237;odo dividido pela popula&#231;&#227;o m&#233;dia entre 2001 e 2021, expressa por 100 mil habitantes. A popula&#231;&#227;o m&#233;dia foi obtida pela soma das estimativas populacionais anuais ao longo do per&#237;odo estudado, dividida pelo n&#250;mero de anos. Essa abordagem foi adotada para reduzir flutua&#231;&#245;es anuais e fornecer estimativas mais est&#225;veis para an&#225;lises em pequenas &#225;reas; no entanto, n&#227;o considera diferen&#231;as na estrutura et&#225;ria entre os munic&#237;pios, o que pode influenciar as compara&#231;&#245;es.</p>
      <p>O desfecho prim&#225;rio do estudo foi a mortalidade por cardiomiopatias, definida a partir do c&#243;digo I42 da Classifica&#231;&#227;o Internacional de Doen&#231;as (CID-10). Inicialmente, realizou-se a an&#225;lise da tend&#234;ncia temporal da mortalidade por anos (<xref ref-type="fig" rid="f02">Figura 1</xref>). Assim, foi avaliada a varia&#231;&#227;o percentual anual (<italic>annual percent change</italic> &#8211; APC) da tend&#234;ncia estudada, com intervalo de confian&#231;a de 95% (IC95%) e signific&#226;ncia estat&#237;stica p&lt;0,05. A an&#225;lise de tend&#234;ncia temporal foi realizada utilizando o programa Joinpoint Regression Program (vers&#227;o 5.0.2, National Cancer Institute, EUA), com regress&#227;o por m&#237;nimos quadrados ponderados, estimando a varia&#231;&#227;o percentual anual (APC) e intervalos de confian&#231;a de 95%. Foi ajustado um modelo log-linear, e o n&#250;mero de pontos de inflex&#227;o (<italic>joinpoints</italic>) foi determinado por meio de testes de permuta&#231;&#227;o, de acordo com as configura&#231;&#245;es padr&#227;o do software. O modelo final selecionado n&#227;o incluiu pontos de inflex&#227;o (ou seja, apresentou uma &#250;nica tend&#234;ncia temporal). Em seguida, foi realizada a an&#225;lise espacial da distribui&#231;&#227;o da mortalidade por cardiomiopatias para cada regi&#227;o do Brasil (<xref ref-type="fig" rid="f03">Figura 2</xref>).</p>
      <p>
        <fig id="f02">
          <label>Figura 1</label>
          <caption>
            <title>&#8211; Padr&#227;o temporal da mortalidade por cardiomiopatia no Brasil entre 2001 e 2021. * Indica que a Varia&#231;&#227;o Percentual Anual (APC) &#233; significativamente diferente de zero no n&#237;vel alfa = 0,05. Modo Final Selecionado: 1 Ponto de inflex&#227;o (Joinpoint). Fonte: Os autores (2025).</title>
          </caption>
          <graphic xlink:href="1678-4170-abc-123-06-e20260064-60064-gf01.tif"/></fig>
      </p>
      <p>
        <fig id="f03">
          <label>Figura 2</label>
          <caption>
            <title>&#8211; Taxa de mortalidade bruta por cardiomiopatia no Brasil, entre 2001 e 2021. Fonte: Os autores (2025).</title>
          </caption>
          <graphic xlink:href="1678-4170-abc-123-06-e20260064-60064-gf02.tif"/></fig>
      </p>
      <p>A suaviza&#231;&#227;o espacial foi realizada utilizando o m&#233;todo bayesiano emp&#237;rico local, com o objetivo de reduzir flutua&#231;&#245;es aleat&#243;rias nas taxas de mortalidade municipais. Foi constru&#237;da uma matriz de pesos espaciais baseada na contiguidade do tipo &#8220;<italic>queen</italic>&#8221; de primeira ordem, na qual munic&#237;pios que compartilham pelo menos uma fronteira ou v&#233;rtice comum foram considerados vizinhos.</p>
      <p>Os limites municipais foram definidos de acordo com a divis&#227;o territorial oficial fornecida pelo Instituto Brasileiro de Geografia e Estat&#237;stica (IBGE), sem qualquer modifica&#231;&#227;o. Os dados de mortalidade do DATASUS foram vinculados aos munic&#237;pios por meio dos c&#243;digos oficiais do IBGE.</p>
      <p>An&#225;lises de sensibilidade utilizando defini&#231;&#245;es alternativas de vizinhan&#231;a (como contiguidade do tipo &#8220;<italic>rook</italic>&#8221;) n&#227;o foram realizadas. No entanto, a escolha da contiguidade <italic>queen</italic> foi adotada por apresentar uma estrutura de vizinhan&#231;a mais abrangente, amplamente utilizada em estudos epidemiol&#243;gicos espaciais e que fornece estimativas mais est&#225;veis em &#225;reas com configura&#231;&#245;es territoriais heterog&#234;neas.</p>
      <p>As an&#225;lises espaciais e a constru&#231;&#227;o dos mapas tem&#225;ticos foram realizadas utilizando o software GeoDa, vers&#227;o 1.22.0.4 (2023). A categoriza&#231;&#227;o das legendas dos mapas seguiu diferentes abordagens conforme o tipo de an&#225;lise realizada. Para as taxas brutas de mortalidade (<xref ref-type="fig" rid="f02">Figuras 1</xref> e <xref ref-type="fig" rid="f03">2</xref>), os valores foram classificados por quantis, permitindo uma distribui&#231;&#227;o equilibrada dos munic&#237;pios entre as categorias e melhorando a visualiza&#231;&#227;o e a comparabilidade. Para as taxas suavizadas obtidas pelo m&#233;todo bayesiano emp&#237;rico local (<xref ref-type="fig" rid="f04">Figuras 3</xref>&#8211;<xref ref-type="fig" rid="f06">5</xref>), a classifica&#231;&#227;o refletiu a distribui&#231;&#227;o dos valores ajustados, com o objetivo de evidenciar padr&#245;es espaciais ap&#243;s a redu&#231;&#227;o das flutua&#231;&#245;es aleat&#243;rias. Essas estrat&#233;gias de classifica&#231;&#227;o s&#227;o comumente utilizadas em an&#225;lises espaciais explorat&#243;rias; no entanto, diferentes m&#233;todos de categoriza&#231;&#227;o podem levar a varia&#231;&#245;es na interpreta&#231;&#227;o visual dos padr&#245;es espaciais.</p>
      <p>
        <fig id="f04">
          <label>Figura 3</label>
          <caption>
            <title>&#8211; Taxa de mortalidade suavizada pelo m&#233;todo bayesiano emp&#237;rico local por cardiomiopatia no Brasil entre 2001 e 2021. Fonte: Os autores (2025).</title>
          </caption>
          <graphic xlink:href="1678-4170-abc-123-06-e20260064-60064-gf03.tif"/></fig>
      </p>
      <p>
        <fig id="f06">
          <label>Figura 5</label>
          <caption>
            <title>&#8211; Signific&#226;ncia estat&#237;stica dos aglomerados espaciais. Fonte: Os autores (2025).</title>
          </caption>
          <graphic xlink:href="1678-4170-abc-123-06-e20260064-60064-gf04.tif"/></fig>
      </p>
      <p>Para a identifica&#231;&#227;o de aglomerados espaciais, utilizou-se o &#205;ndice de Moran Global e Local. O &#205;ndice de Moran Global mede a correla&#231;&#227;o entre vizinhos de primeira ordem e foi usado para testar a hip&#243;tese de depend&#234;ncia espacial. O m&#233;todo identifica a autocorrela&#231;&#227;o espacial e pode variar entre -1 e +1, no qual os valores pr&#243;ximos a zero indicam aus&#234;ncia de depend&#234;ncia espacial, considerando-se significante p&lt;0,05. Caso a hip&#243;tese de depend&#234;ncia seja aceita, utiliza-se o &#205;ndice de Moran Local (LISA, do ingl&#234;s <italic>Local Index of Spatial Association</italic>) para observar a presen&#231;a de agregados espaciais, dado p&lt;0,05. Os resultados das an&#225;lises descritas acima foram demonstrados pelo Moran Map e LISA Map (<xref ref-type="fig" rid="f05">Figuras 4</xref> e <xref ref-type="fig" rid="f06">5</xref>). O Moran Map demonstra graficamente o grau de similaridade entre vizinhos, sendo representado por quatro quadrantes:</p>
      <p>
        <fig id="f05">
          <label>Figura 4</label>
          <caption>
            <title>&#8211; Aglomerados espaciais de mortalidade por cardiomiopatia no Brasil entre 2001 e 2021. Fonte: Os autores (2025).</title>
          </caption>
          <graphic xlink:href="1678-4170-abc-123-06-e20260064-60064-gf05.tif"/></fig>
      </p>
      <list list-type="order">
        <list-item>
          <p>Alto-alto (quadrante superior direito): Correspondem a munic&#237;pios que possuem altas taxas de mortalidade e est&#227;o pr&#243;ximos a munic&#237;pios que tamb&#233;m possuem altas taxas de mortalidade.</p>
        </list-item>
        <list-item>
          <p>Baixo-baixo (quadrante inferior esquerdo): Correspondem a munic&#237;pios que possuem baixas taxas de mortalidade e est&#227;o pr&#243;ximos a munic&#237;pios que tamb&#233;m possuem baixas taxas de mortalidade.</p>
        </list-item>
        <list-item>
          <p>Alto-baixo (quadrante inferior direito): Correspondem a munic&#237;pios que possuem altas taxas de mortalidade e est&#227;o pr&#243;ximos a munic&#237;pios que possuem baixas taxas de mortalidade.</p>
        </list-item>
        <list-item>
          <p>Baixo-alto (quadrante superior esquerdo): Correspondem a munic&#237;pios que possuem baixas taxas de mortalidade e est&#227;o pr&#243;ximos a munic&#237;pios que possuem altas taxas de mortalidade.</p>
        </list-item>
      </list>
      <p>Os dados utilizados s&#227;o de acesso p&#250;blico, agregados e sem identifica&#231;&#227;o individual, n&#227;o havendo a possibilidade de danos f&#237;sicos ou morais na perspectiva do indiv&#237;duo e da coletividade. Desse modo, o estudo dispensa aprecia&#231;&#227;o por Comit&#234; de &#201;tica em Pesquisa, estando em conformidade com a Resolu&#231;&#227;o n&#186; 466/2012 do Conselho Nacional de Sa&#250;de. Al&#233;m disso, haja vista que o estudo &#233; baseado em dados secund&#225;rios, os achados podem refletir limita&#231;&#245;es relacionadas &#224; qualidade da informa&#231;&#227;o, como sub-registro e dados incompletos nas declara&#231;&#245;es de &#243;bito.</p>
    </sec>
    <sec sec-type="results">
      <title>Resultados</title>
      <sec>
        <title>Perfil epidemiol&#243;gico dos dados</title>
        <p>Entre 2001 e 2021, foram registrados no Brasil 272.448 &#243;bitos por cardiomiopatias. Como &#233; mostrado na <xref ref-type="fig" rid="f01">Figura Central</xref>, a caracteriza&#231;&#227;o sociodemogr&#225;fica evidenciou maior ocorr&#234;ncia de &#243;bitos entre indiv&#237;duos do sexo masculino e com idade igual ou superior a 80 anos. Observou-se ainda predom&#237;nio entre pessoas autodeclaradas brancas, casadas e com n&#237;vel de escolaridade entre 1 a 3 anos. O perfil epidemiol&#243;gico detalhado dos &#243;bitos encontra-se descrito na <xref ref-type="table" rid="t1">Tabela 1</xref>.</p>
        <p>
          <table-wrap id="t1">
            <label>Tabela 1</label>
            <caption>
              <title>&#8211; Perfil epidemiol&#243;gico dos indiv&#237;duos que foram a &#243;bito por cardiomiopatia no Brasil entre 2001 e 2021</title>
            </caption>
            <table frame="hsides" rules="groups">
              <colgroup width="33%">
                <col/>
                <col/>
                <col/>
              </colgroup>
              <thead>
                <tr>
                  <th align="left">Vari&#225;vel</th>
                  <th>n</th>
                  <th>%</th>
                </tr>
              </thead>
              <tbody>
                <tr>
                  <td colspan="3">
                    <bold>Sexo</bold>
                  </td>
                </tr>
                <tr>
                  <td>Masculino</td>
                  <td align="center">159.782</td>
                  <td align="center">58,65</td>
                </tr>
                <tr>
                  <td>Feminino</td>
                  <td align="center">112.618</td>
                  <td align="center">41,33</td>
                </tr>
                <tr>
                  <td>Ausente</td>
                  <td align="center">48</td>
                  <td align="center">0,02</td>
                </tr>
                <tr>
                  <td colspan="3">
                    <bold>Faixa et&#225;ria (anos)</bold>
                  </td>
                </tr>
                <tr>
                  <td>&lt;1</td>
                  <td align="center">2.611</td>
                  <td align="center">0,96</td>
                </tr>
                <tr>
                  <td>1-4</td>
                  <td align="center">1.327</td>
                  <td align="center">0,49</td>
                </tr>
                <tr>
                  <td>5-9</td>
                  <td align="center">572</td>
                  <td align="center">0,21</td>
                </tr>
                <tr>
                  <td>10-14</td>
                  <td align="center">834</td>
                  <td align="center">0,31</td>
                </tr>
                <tr>
                  <td>15-19</td>
                  <td align="center">1.664</td>
                  <td align="center">0,61</td>
                </tr>
                <tr>
                  <td>20-29</td>
                  <td align="center">5.635</td>
                  <td align="center">2,07</td>
                </tr>
                <tr>
                  <td>30-39</td>
                  <td align="center">12.571</td>
                  <td align="center">4,61</td>
                </tr>
                <tr>
                  <td>40-49</td>
                  <td align="center">24.747</td>
                  <td align="center">9,08</td>
                </tr>
                <tr>
                  <td>50-59</td>
                  <td align="center">39.897</td>
                  <td align="center">14,64</td>
                </tr>
                <tr>
                  <td>60-69</td>
                  <td align="center">51.505</td>
                  <td align="center">18,90</td>
                </tr>
                <tr>
                  <td>70-79</td>
                  <td align="center">59.978</td>
                  <td align="center">22,01</td>
                </tr>
                <tr>
                  <td>&#8805;80</td>
                  <td align="center">70.132</td>
                  <td align="center">25,75</td>
                </tr>
                <tr>
                  <td>Idade ausente</td>
                  <td align="center">975</td>
                  <td align="center">0,36</td>
                </tr>
                <tr>
                  <td colspan="3">
                    <bold>Cor/ra&#231;a</bold>
                  </td>
                </tr>
                <tr>
                  <td>Branca</td>
                  <td align="center">142.166</td>
                  <td align="center">52,18</td>
                </tr>
                <tr>
                  <td>Preta</td>
                  <td align="center">25.990</td>
                  <td align="center">9,54</td>
                </tr>
                <tr>
                  <td>Parda</td>
                  <td align="center">86.935</td>
                  <td align="center">31,91</td>
                </tr>
                <tr>
                  <td>Amarela</td>
                  <td align="center">1.341</td>
                  <td align="center">0,49</td>
                </tr>
                <tr>
                  <td>Ind&#237;gena</td>
                  <td align="center">380</td>
                  <td align="center">0,14</td>
                </tr>
                <tr>
                  <td>Ausente</td>
                  <td align="center">15.636</td>
                  <td align="center">5,74</td>
                </tr>
                <tr>
                  <td colspan="3">
                    <bold>Estado civil</bold>
                  </td>
                </tr>
                <tr>
                  <td>Solteiro</td>
                  <td align="center">66.998</td>
                  <td align="center">24,59</td>
                </tr>
                <tr>
                  <td>Casado</td>
                  <td align="center">100.189</td>
                  <td align="center">36,77</td>
                </tr>
                <tr>
                  <td>Vi&#250;vo</td>
                  <td align="center">66.280</td>
                  <td align="center">24,33</td>
                </tr>
                <tr>
                  <td>Separado judicialmente</td>
                  <td align="center">15.681</td>
                  <td align="center">5,76</td>
                </tr>
                <tr>
                  <td>Outro</td>
                  <td align="center">3.344</td>
                  <td align="center">1,23</td>
                </tr>
                <tr>
                  <td>Ausente</td>
                  <td align="center">19.956</td>
                  <td align="center">7,32</td>
                </tr>
                <tr>
                  <td colspan="3">
                    <bold>Escolaridade (anos)</bold>
                  </td>
                </tr>
                <tr>
                  <td>Nenhuma</td>
                  <td align="center">39.515</td>
                  <td align="center">14,50</td>
                </tr>
                <tr>
                  <td>1-3</td>
                  <td align="center">59.684</td>
                  <td align="center">21,91</td>
                </tr>
                <tr>
                  <td>4-7</td>
                  <td align="center">56.125</td>
                  <td align="center">20,60</td>
                </tr>
                <tr>
                  <td>8-11</td>
                  <td align="center">29.699</td>
                  <td align="center">10,90</td>
                </tr>
                <tr>
                  <td>&#8805;12</td>
                  <td align="center">10.933</td>
                  <td align="center">4,01</td>
                </tr>
                <tr>
                  <td>Ausente</td>
                  <td align="center">76.492</td>
                  <td align="center">28,08</td>
                </tr>
                <tr>
                  <td colspan="3">
                    <bold>Local de ocorr&#234;ncia do &#243;bito</bold>
                  </td>
                </tr>
                <tr>
                  <td>Hospital</td>
                  <td align="center">168.444</td>
                  <td align="center">61,83</td>
                </tr>
                <tr>
                  <td>Outro estabelecimento de sa&#250;de</td>
                  <td align="center">16.890</td>
                  <td align="center">6,20</td>
                </tr>
                <tr>
                  <td>Domic&#237;lio</td>
                  <td align="center">72.962</td>
                  <td align="center">26,78</td>
                </tr>
                <tr>
                  <td>Via p&#250;blica</td>
                  <td align="center">4.852</td>
                  <td align="center">1,78</td>
                </tr>
                <tr>
                  <td>Outros</td>
                  <td align="center">8.948</td>
                  <td align="center">3,28</td>
                </tr>
                <tr>
                  <td>Ausente</td>
                  <td align="center">352</td>
                  <td align="center">0,13</td>
                </tr>
              </tbody>
            </table>
            <table-wrap-foot>
              <attrib>Fonte: Departamento de Inform&#225;tica do Sistema &#218;nico de Sa&#250;de (DATASUS), Minist&#233;rio da Sa&#250;de, Brasil.</attrib>
            </table-wrap-foot>
          </table-wrap>
        </p>
        <p>Em rela&#231;&#227;o ao local de ocorr&#234;ncia, a maioria dos &#243;bitos ocorreu em ambiente hospitalar, seguida por &#243;bitos em domic&#237;lio, enquanto as demais categorias apresentaram menor frequ&#234;ncia relativa (<xref ref-type="table" rid="t1">Tabela 1</xref>).</p>
      </sec>
      <sec>
        <title>An&#225;lise temporal da mortalidade</title>
        <p>A taxa m&#233;dia de mortalidade por cardiomiopatias durante o per&#237;odo do estudo foi de 6,66 &#243;bitos por 100.000 habitantes, sendo a maior taxa observada em 2004 e a menor em 2020. A an&#225;lise de tend&#234;ncia temporal demonstrou uma redu&#231;&#227;o significativa da mortalidade ao longo do per&#237;odo estudado, com uma diminui&#231;&#227;o m&#233;dia anual de 1,86% (APC = &#8722;1,86%; IC 95% [&#8722;2,73; &#8722;1,03], p &lt; 0,001). O modelo final n&#227;o incluiu pontos de inflex&#227;o (joinpoints), indicando uma tend&#234;ncia constante de queda ao longo do tempo, conforme demonstrado na <xref ref-type="fig" rid="f01">Figura Central</xref> e na <xref ref-type="fig" rid="f02">Figura 1</xref>.</p>
      </sec>
      <sec>
        <title>An&#225;lise temporal da mortalidade</title>
        <p>A taxa m&#233;dia de mortalidade por cardiomiopatias durante o per&#237;odo do estudo foi de 6,66 &#243;bitos por 100.000 habitantes, sendo a maior taxa observada em 2004 e a menor em 2020. A an&#225;lise de tend&#234;ncia temporal demonstrou uma redu&#231;&#227;o significativa da mortalidade ao longo do per&#237;odo estudado, com uma diminui&#231;&#227;o m&#233;dia anual de 1,86% (APC = &#8722;1,86%; IC 95% [&#8722;2,73; &#8722;1,03], p &lt; 0,001). O modelo final n&#227;o incluiu pontos de inflex&#227;o (joinpoints), indicando uma tend&#234;ncia constante de queda ao longo do tempo, conforme demonstrado na <xref ref-type="fig" rid="f01">Figura Central</xref> e na <xref ref-type="fig" rid="f02">Figura 1</xref>.</p>
      </sec>
      <sec>
        <title>An&#225;lise espacial da mortalidade</title>
        <p>A an&#225;lise espacial da taxa bruta de mortalidade evidenciou heterogeneidade na distribui&#231;&#227;o dos &#243;bitos por cardiomiopatias no territ&#243;rio nacional, com maiores taxas concentradas predominantemente nas regi&#245;es Centro-Oeste, Sul e Sudeste (<xref ref-type="fig" rid="f03">Figura 2</xref>). Os munic&#237;pios com as maiores taxas de mortalidade no per&#237;odo inclu&#237;ram Corumb&#225; de Goi&#225;s, S&#227;o Jos&#233; do Rio Pardo, Itajobi, Santo In&#225;cio e S&#227;o Manuel.</p>
        <p>Ap&#243;s a aplica&#231;&#227;o da suaviza&#231;&#227;o bayesiana emp&#237;rica local, observou-se um padr&#227;o espacial mais definido, com agrega&#231;&#227;o de munic&#237;pios com elevadas taxas de mortalidade principalmente nas regi&#245;es Centro-Oeste e Sudeste do Brasil (<xref ref-type="fig" rid="f04">Figura 3</xref>). A an&#225;lise de autocorrela&#231;&#227;o espacial confirmou a presen&#231;a de depend&#234;ncia espacial significativa, evidenciada pelo &#205;ndice de Moran Global positivo (I = 0,864; p = 0,01).</p>
        <p>A an&#225;lise local de autocorrela&#231;&#227;o (LISA) identificou aglomerados espaciais estatisticamente significativos. Os aglomerados do tipo alto-alto foram observados principalmente nas regi&#245;es Centro-Oeste, Sul e Sudeste, enquanto os aglomerados baixo-baixo concentraram-se predominantemente nas regi&#245;es Norte e Nordeste do pa&#237;s (<xref ref-type="fig" rid="f01">Figura Central</xref>, <xref ref-type="fig" rid="f05">Figuras 4</xref> e <xref ref-type="fig" rid="f06">5</xref>).</p>
      </sec>
    </sec>
    <sec sec-type="discussion">
      <title>Discuss&#227;o</title>
      <p>Os achados deste estudo demonstram que, entre 2001 e 2021, foram registrados 272.448 &#243;bitos por cardiomiopatias no Brasil, com predomin&#226;ncia entre homens (58,65%), indiv&#237;duos com 80 anos ou mais (25,74%) e pessoas autodeclaradas brancas (52,18%). Esse perfil epidemiol&#243;gico &#233; consistente com dados previamente descritos na literatura, que apontam maior gravidade e letalidade das cardiomiopatias em popula&#231;&#245;es idosas e do sexo masculino, associadas ao envelhecimento cardiovascular e &#224; maior carga de comorbidades, como hipertens&#227;o arterial e diabetes mellitus.<sup><xref ref-type="bibr" rid="B2">2</xref>,<xref ref-type="bibr" rid="B5">5</xref>,<xref ref-type="bibr" rid="B20">20</xref></sup></p>
      <p>A maior mortalidade observada entre homens pode ser explicada, em parte, por diferen&#231;as biol&#243;gicas e hormonais, al&#233;m de maior exposi&#231;&#227;o a fatores de risco e maior propens&#227;o a altera&#231;&#245;es estruturais do mioc&#225;rdio e eventos cardiovasculares, como a morte s&#250;bita, o que est&#225; alinhado com achados de estudos internacionais e diretrizes cl&#237;nicas.<sup><xref ref-type="bibr" rid="B2">2</xref>,<xref ref-type="bibr" rid="B6">6</xref>,<xref ref-type="bibr" rid="B7">7</xref></sup> Ademais, referente &#224; faixa et&#225;ria, o envelhecimento populacional &#233; um dos principais fatores de risco para o desenvolvimento da patologia, especialmente no caso da cardiomiopatia dilatada, devido ao aumento da vulnerabilidade mioc&#225;rdica e da preval&#234;ncia das comorbidades supracitadas, refor&#231;ando a relev&#226;ncia desse agravo em pa&#237;ses com transi&#231;&#227;o demogr&#225;fica avan&#231;ada, como o Brasil.<sup><xref ref-type="bibr" rid="B2">2</xref>,<xref ref-type="bibr" rid="B5">5</xref>,<xref ref-type="bibr" rid="B7">7</xref>,<xref ref-type="bibr" rid="B20">20</xref></sup></p>
      <p>Menor n&#237;vel de escolaridade foi associado a maior mortalidade por cardiomiopatias: aproximadamente 21,91% dos indiv&#237;duos que faleceram devido a patologia tinham de 1 a 3 anos de estudo. Esse dado dialoga com o que foi identificado por Santos et al.,<sup><xref ref-type="bibr" rid="B14">14</xref></sup> que sugere a influ&#234;ncia dos determinantes sociais da sa&#250;de nos desfechos cardiovasculares, incluindo menor acesso a servi&#231;os especializados, diagn&#243;stico tardio e menor ades&#227;o terap&#234;utica. Al&#233;m disso, a predomin&#226;ncia de &#243;bitos entre indiv&#237;duos casados (36,77%) pode refletir a maior longevidade observada nesse grupo, fen&#244;meno j&#225; descrito em an&#225;lises demogr&#225;ficas brasileiras.<sup><xref ref-type="bibr" rid="B21">21</xref>,<xref ref-type="bibr" rid="B22">22</xref></sup></p>
      <p>Quanto aos locais de ocorr&#234;ncia dos &#243;bitos, a maior parte ocorreu em ambiente hospitalar (61,83%), o que pode indicar n&#227;o s&#243; o aumento do acesso a interna&#231;&#245;es, mas tamb&#233;m o diagn&#243;stico tardio das cardiomiopatias, haja vista que muitas vezes passam despercebidas nos est&#225;gios iniciais.<sup><xref ref-type="bibr" rid="B6">6</xref></sup> Em contrapartida, a propor&#231;&#227;o significativa de mortes que ocorreram em domic&#237;lio (26,78%) sugere fragilidades no acompanhamento ambulatorial e no manejo precoce da patologia, especialmente em regi&#245;es com limita&#231;&#245;es geogr&#225;ficas e sociais importantes, conforme j&#225; destacado por Ziaeian e Fonarow.<sup><xref ref-type="bibr" rid="B2">2</xref></sup></p>
      <p>A redu&#231;&#227;o observada na mortalidade por cardiomiopatias (diminui&#231;&#227;o m&#233;dia anual de 1,86%; p &lt; 0,05) pode estar parcialmente relacionada aos avan&#231;os terap&#234;uticos das &#250;ltimas d&#233;cadas, incluindo o uso de inibidores da enzima conversora de angiotensina, betabloqueadores, dispositivos implant&#225;veis e transplante card&#237;aco. No entanto, o acesso a essas terapias permanece heterog&#234;neo no Brasil, e seu impacto na mortalidade em n&#237;vel populacional provavelmente varia de acordo com a infraestrutura regional de sa&#250;de e a disponibilidade de servi&#231;os especializados.<sup><xref ref-type="bibr" rid="B9">9</xref>,<xref ref-type="bibr" rid="B10">10</xref></sup> As diretrizes cl&#237;nicas atuais enfatizam que o diagn&#243;stico precoce e o tratamento multidisciplinar s&#227;o essenciais para reduzir a mortalidade.<sup><xref ref-type="bibr" rid="B9">9</xref></sup> Ainda assim, a persist&#234;ncia de n&#237;veis elevados de mortalidade sugere que os benef&#237;cios desses avan&#231;os n&#227;o est&#227;o distribu&#237;dos de forma uniforme no territ&#243;rio nacional. O pico observado em 2004 pode refletir melhorias na detec&#231;&#227;o e notifica&#231;&#227;o dos casos durante a fase inicial de consolida&#231;&#227;o do Sistema de Informa&#231;&#245;es sobre Mortalidade (SIM), bem como flutua&#231;&#245;es temporais inerentes &#224;s an&#225;lises baseadas em popula&#231;&#227;o.<sup><xref ref-type="bibr" rid="B23">23</xref>,<xref ref-type="bibr" rid="B24">24</xref></sup></p>
      <p>Notavelmente, a redu&#231;&#227;o da mortalidade observada em 2020, seguida por um aumento parcial em 2021, deve ser interpretada no contexto da pandemia de COVID-19. Esse per&#237;odo foi marcado por interrup&#231;&#245;es no acesso aos servi&#231;os de sa&#250;de, atrasos no diagn&#243;stico e poss&#237;vel classifica&#231;&#227;o incorreta das causas de morte, especialmente para condi&#231;&#245;es cardiovasculares. Estudos pr&#233;vios no Brasil demonstraram que o excesso de mortalidade durante a pandemia pode estar associado &#224; subnotifica&#231;&#227;o, erros diagn&#243;sticos e &#224; redu&#231;&#227;o do acesso ao cuidado para condi&#231;&#245;es n&#227;o relacionadas &#224; COVID-19.<sup><xref ref-type="bibr" rid="B25">25</xref></sup></p>
      <p>Nesse contexto, a an&#225;lise espacial evidenciou diferen&#231;as importantes entre as regi&#245;es do pa&#237;s. Aglomerados de alta mortalidade foram observados principalmente nas regi&#245;es Centro-Oeste, Sul e Sudeste, com destaque para munic&#237;pios como Corumb&#225; de Goi&#225;s e S&#227;o Jos&#233; do Rio Pardo. Esse cen&#225;rio sugere a influ&#234;ncia de fatores demogr&#225;ficos, como a maior propor&#231;&#227;o de popula&#231;&#227;o envelhecida nessas &#225;reas, associada &#224; maior preval&#234;ncia de fatores de risco cardiovasculares. Adicionalmente, a maior capacidade diagn&#243;stica e de notifica&#231;&#227;o dos casos nessas regi&#245;es pode contribuir para as taxas mais elevadas observadas. O alto valor do I de Moran Global observado neste estudo pode refletir o uso de taxas suavizadas e o forte padr&#227;o de agrupamento regional no Brasil. Al&#233;m disso, o uso de taxas m&#233;dias populacionais pode ter contribu&#237;do para o aumento da estabilidade espacial e da autocorrela&#231;&#227;o. Tamb&#233;m &#233; importante considerar que valores extremos observados em munic&#237;pios espec&#237;ficos podem ser influenciados por pequenos tamanhos populacionais, levando &#224; instabilidade das taxas, bem como por estruturas locais de sa&#250;de ou padr&#245;es de notifica&#231;&#227;o.<sup><xref ref-type="bibr" rid="B11">11</xref></sup></p>
      <p>Em contrapartida, nas regi&#245;es Norte e Nordeste, onde foram identificados aglomerados de baixa mortalidade, &#233; plaus&#237;vel que os achados n&#227;o reflitam necessariamente menor ocorr&#234;ncia da doen&#231;a, mas sim subdiagn&#243;stico e/ou sub-registro. Nessas regi&#245;es, o acesso a exames diagn&#243;sticos como o ecocardiograma, o qual &#233; essencial para a avalia&#231;&#227;o da fun&#231;&#227;o mioc&#225;rdica, ainda &#233; limitado, e os sistemas de notifica&#231;&#227;o de &#243;bitos apresentam fragilidades.<sup><xref ref-type="bibr" rid="B7">7</xref>,<xref ref-type="bibr" rid="B14">14</xref></sup> Nesse sentido, um estudo realizado no estado do Cear&#225; demonstrou que munic&#237;pios de menor porte populacional e com menor infraestrutura assistencial tendem a apresentar piores desfechos por doen&#231;as cardiovasculares, o que fortalece essa interpreta&#231;&#227;o.<sup><xref ref-type="bibr" rid="B14">14</xref></sup> Esses achados refor&#231;am a necessidade de cautela na interpreta&#231;&#227;o das taxas e evidenciam desigualdades regionais relevantes.</p>
      <p>Essas desigualdades regionais refor&#231;am a influ&#234;ncia dos determinantes sociais da sa&#250;de e das fragilidades do sistema de aten&#231;&#227;o &#224;s doen&#231;as cr&#244;nicas sobre os desfechos dos pacientes.<sup><xref ref-type="bibr" rid="B26">26</xref></sup> Nesse contexto, destaca-se o papel da Aten&#231;&#227;o Prim&#225;ria &#224; Sa&#250;de (APS) na vigil&#226;ncia e no rastreamento precoce das cardiomiopatias.<sup><xref ref-type="bibr" rid="B27">27</xref></sup> Logo, a amplia&#231;&#227;o do acesso a exames diagn&#243;sticos, como o ecocardiograma, e o investimento na capacita&#231;&#227;o de profissionais da rede p&#250;blica s&#227;o estrat&#233;gias recomendadas pelas diretrizes vigentes e potencialmente relevantes para melhorar os desfechos em n&#237;vel nacional.<sup><xref ref-type="bibr" rid="B9">9</xref></sup></p>
      <p>Embora o presente estudo n&#227;o tenha avaliado diretamente indicadores socioecon&#244;micos espec&#237;ficos, como desigualdade de renda (por exemplo, coeficiente de Gini), &#205;ndice de Desenvolvimento Humano (IDH), gastos em sa&#250;de ou cobertura da aten&#231;&#227;o prim&#225;ria, esses fatores podem explicar parcialmente as disparidades regionais observadas. Estudos pr&#233;vios no Brasil demonstraram que &#225;reas com menor desenvolvimento socioecon&#244;mico e acesso reduzido aos servi&#231;os de sa&#250;de tendem a apresentar piores desfechos cardiovasculares, incluindo maiores taxas de mortalidade.<sup><xref ref-type="bibr" rid="B1">1</xref>-<xref ref-type="bibr" rid="B3">3</xref></sup> Em particular, diferen&#231;as na cobertura da aten&#231;&#227;o prim&#225;ria e no acesso a recursos diagn&#243;sticos especializados podem contribuir para atrasos no diagn&#243;stico e no tratamento, influenciando os padr&#245;es de mortalidade entre as regi&#245;es.<sup><xref ref-type="bibr" rid="B28">28</xref>-<xref ref-type="bibr" rid="B30">30</xref></sup></p>
      <p>Outra considera&#231;&#227;o importante &#233; a heterogeneidade cl&#237;nica e etiol&#243;gica abrangida pelo c&#243;digo I42 da CID-10, que inclui diferentes subtipos de cardiomiopatia, como dilatada, hipertr&#243;fica, restritiva e formas secund&#225;rias, com mecanismos fisiopatol&#243;gicos e progn&#243;sticos distintos.<sup><xref ref-type="bibr" rid="B3">3</xref></sup> Quando analisada de forma agregada, essa heterogeneidade pode obscurecer padr&#245;es epidemiol&#243;gicos espec&#237;ficos de cada subtipo e levar a interpreta&#231;&#245;es simplificadas das tend&#234;ncias de mortalidade.</p>
      <p>Ademais, devem ser considerados o subdiagn&#243;stico e a subnotifica&#231;&#227;o, especialmente em regi&#245;es com acesso limitado a ferramentas diagn&#243;sticas especializadas, como ecocardiografia e resson&#226;ncia magn&#233;tica card&#237;aca, bem como poss&#237;veis limita&#231;&#245;es na qualidade do preenchimento das declara&#231;&#245;es de &#243;bito. As disparidades regionais no acesso a servi&#231;os de cardiologia e a variabilidade nos sistemas de informa&#231;&#227;o em sa&#250;de podem contribuir para classifica&#231;&#245;es incorretas e para uma maior propor&#231;&#227;o de causas de morte mal definidas.<sup><xref ref-type="bibr" rid="B31">31</xref></sup></p>
      <p>Adicionalmente, as mortes relacionadas &#224;s cardiomiopatias podem estar subestimadas devido ao registro frequente da insufici&#234;ncia card&#237;aca (c&#243;digo I50 da CID-10) como causa b&#225;sica de &#243;bito, mesmo quando decorrente de cardiomiopatias (c&#243;digo I42 da CID-10). Essa limita&#231;&#227;o &#233; refor&#231;ada pela incapacidade da plataforma DATASUS de identificar ou cruzar causas associadas de &#243;bito.<sup><xref ref-type="bibr" rid="B31">31</xref></sup></p>
      <p>A cardiomiopatia chag&#225;sica, uma causa relevante de doen&#231;a mioc&#225;rdica no Brasil, &#233; classificada sob um c&#243;digo diferente da CID-10 (B57.2) e pode n&#227;o ser totalmente capturada em an&#225;lises restritas ao c&#243;digo I42.<sup><xref ref-type="bibr" rid="B32">32</xref></sup> Consequentemente, &#225;reas identificadas com menores taxas de mortalidade podem refletir limita&#231;&#245;es na capacidade diagn&#243;stica e na qualidade dos dados, em vez de uma real redu&#231;&#227;o da carga da doen&#231;a, exigindo cautela na interpreta&#231;&#227;o de an&#225;lises espa&#231;o-temporais.<sup><xref ref-type="bibr" rid="B31">31</xref></sup></p>
      <p>O uso de dados secund&#225;rios representa uma limita&#231;&#227;o deste estudo e pode introduzir vieses relacionados &#224; subnotifica&#231;&#227;o, informa&#231;&#245;es incompletas e inconsist&#234;ncias no registro das causas de morte.<sup><xref ref-type="bibr" rid="B32">32</xref>-<xref ref-type="bibr" rid="B34">34</xref></sup> An&#225;lises baseadas em estimativas populacionais podem ser afetadas pela defasagem temporal do censo demogr&#225;fico mais recente (2010), potencialmente influenciando a acur&#225;cia das estimativas da taxa de mortalidade.<sup><xref ref-type="bibr" rid="B34">34</xref></sup> Como se trata de um estudo ecol&#243;gico, os achados est&#227;o sujeitos ao vi&#233;s de agrega&#231;&#227;o e podem n&#227;o refletir associa&#231;&#245;es em n&#237;vel individual.<sup><xref ref-type="bibr" rid="B35">35</xref></sup> Em acr&#233;scimo, a an&#225;lise considerou apenas a causa b&#225;sica de &#243;bito, o que pode subestimar a mortalidade relacionada &#224;s cardiomiopatias quando essas condi&#231;&#245;es s&#227;o registradas como causas associadas.<sup><xref ref-type="bibr" rid="B36">36</xref></sup> A aus&#234;ncia de padroniza&#231;&#227;o por idade tamb&#233;m pode limitar compara&#231;&#245;es entre regi&#245;es, considerando a forte depend&#234;ncia da mortalidade por cardiomiopatias em rela&#231;&#227;o &#224; idade e as diferen&#231;as regionais na estrutura et&#225;ria da popula&#231;&#227;o.<sup><xref ref-type="bibr" rid="B37">37</xref></sup> Por fim, o uso de taxas m&#233;dias de mortalidade ao longo de um per&#237;odo prolongado pode obscurecer varia&#231;&#245;es temporais e mudan&#231;as demogr&#225;ficas, potencialmente influenciando a interpreta&#231;&#227;o dos padr&#245;es espaciais.<sup><xref ref-type="bibr" rid="B34">34</xref></sup></p>
      <p>Uma limita&#231;&#227;o adicional refere-se ao potencial de erro do tipo I devido a m&#250;ltiplas compara&#231;&#245;es em an&#225;lises espaciais envolvendo um grande n&#250;mero de munic&#237;pios. A signific&#226;ncia estat&#237;stica foi estabelecida em p &lt; 0,05 sem ajuste formal para m&#250;ltiplos testes, como a corre&#231;&#227;o pela taxa de descoberta falsa (<italic>False Discovery Rate</italic> &#8211; FDR). Embora essa abordagem possa aumentar a probabilidade de identifica&#231;&#227;o de clusters esp&#250;rios, ela &#233; comumente adotada em an&#225;lises espaciais explorat&#243;rias.<sup><xref ref-type="bibr" rid="B38">38</xref></sup> Logo, os padr&#245;es espaciais identificados devem ser interpretados com cautela.</p>
      <p>Em s&#237;ntese, a an&#225;lise t&#234;mporo-espacial identificou &#225;reas do territ&#243;rio brasileiro com elevadas taxas de mortalidade por cardiomiopatias, evidenciando importantes desigualdades regionais. Dessa forma, os achados do presente estudo sugerem a necessidade de estrat&#233;gias alinhadas &#224; realidade e particularidades locais, voltadas para o fortalecimento da vigil&#226;ncia e do cuidado &#224;s cardiomiopatias. Nesse sentido, os resultados fornecem subs&#237;dios cient&#237;ficos para o planejamento e a organiza&#231;&#227;o de a&#231;&#245;es em sa&#250;de, com &#234;nfase na Aten&#231;&#227;o Prim&#225;ria &#224; Sa&#250;de (APS) principalmente nos locais com maior vulnerabilidade, visando ampliar o acesso e cuidado e, consequentemente, promover sa&#250;de e contribuir para a redu&#231;&#227;o da morbimortalidade por cardiomiopatias no Brasil.</p>
    </sec>
    <sec sec-type="conclusions">
      <title>Conclus&#227;o</title>
      <p>Entre 2001 e 2021, a mortalidade por cardiomiopatias no Brasil apresentou redu&#231;&#227;o temporal significativa, com diminui&#231;&#227;o m&#233;dia anual de 1,86%, embora tenha permanecido expressiva no per&#237;odo analisado. Observou-se maior frequ&#234;ncia de &#243;bitos entre homens, indiv&#237;duos com idade igual ou superior a 80 anos, pessoas autodeclaradas brancas, casadas e com menor escolaridade. A an&#225;lise espacial evidenciou autocorrela&#231;&#227;o espacial positiva e identificou aglomerados de alta mortalidade predominantemente nas regi&#245;es Centro-Oeste, Sul e Sudeste, enquanto aglomerados de baixa mortalidade foram observados principalmente nas regi&#245;es Norte e Nordeste. Esses achados confirmam a heterogeneidade regional da mortalidade por cardiomiopatias no pa&#237;s e demonstram a utilidade da an&#225;lise t&#234;mporo-espacial para a identifica&#231;&#227;o de &#225;reas priorit&#225;rias em sa&#250;de p&#250;blica.</p>
    </sec>
  </body>
  <back>
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          <article-title>Local Indicators of Spatial Association-LISA</article-title>
          <source>Geogr Anal</source>
          <year>1995</year>
          <volume>27</volume>
          <issue>2</issue>
          <fpage>93</fpage>
          <lpage>115</lpage>
          <pub-id pub-id-type="doi">10.1111/j.1538-4632.1995.tb00338.x</pub-id>
        </element-citation>
      </ref>
    </ref-list>
    <fn-group>
      <fn fn-type="other">
        <label>Vincula&#231;&#227;o Acad&#234;mica</label>
        <p> N&#227;o h&#225; vincula&#231;&#227;o deste estudo a programas de p&#243;s-gradua&#231;&#227;o.</p>
      </fn>
      <fn fn-type="other">
        <label>Aprova&#231;&#227;o &#201;tica e Consentimento Informado</label>
        <p> Este artigo n&#227;o cont&#233;m estudos com humanos ou animais realizados por nenhum dos autores.</p>
      </fn>
      <fn fn-type="other">
        <label>Uso de Intelig&#234;ncia Artificial</label>
        <p> Durante a prepara&#231;&#227;o deste trabalho, o(s) autor(es) usaram Deepl Translate para sistema de tradu&#231;&#227;o autom&#225;tica baseada em redes neurais, amplamente reconhecido por sua qualidade de sa&#237;da lingu&#237;stica. Ap&#243;s o uso desta ferramenta/servi&#231;o, o(s) autor(es) revisaram e editaram o conte&#250;do conforme necess&#225;rio e assumem total responsabilidade pelo conte&#250;do do artigo publicado.</p>
      </fn>
      <fn fn-type="data-availability" specific-use="data-available">
        <label>Disponibilidade de Dados</label>
        <p> Os conte&#250;dos est&#227;o dispon&#237;veis no link [<ext-link ext-link-type="uri" xlink:href="http://tabnet.datasus.gov.br/">http://tabnet.datasus.gov.br/</ext-link>]. Tabnet _ DATASUS. Departamento de Inform&#225;tica do Sistema &#218;nico de Sa&#250;de.</p>
      </fn>
      <fn fn-type="financial-disclosure">
        <label>Fontes de Financiamento</label>
        <p> O presente estudo n&#227;o teve fontes de financiamento externas.</p>
      </fn>
    </fn-group>
  </back>
  <sub-article article-type="translation" id="TRen" xml:lang="en">
    <front-stub>
      <article-id pub-id-type="doi">10.36660/abc.20260064i</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Original Article</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Spatiotemporal Analysis of Mortality from Cardiomyopathies in Brazil between 2001 and 2021</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0000-0002-6546-9841</contrib-id>
          <name>
            <surname>Kubrusly</surname>
            <given-names>Luiz Fernando</given-names>
          </name>
          <role>Conception and design of the research</role>
          <role>Acquisition of data</role>
          <role>Analysis and interpretation of the data</role>
          <role>Writing of the manuscript</role>
          <role>Critical revision of the manuscript for intellectual content</role>
          <xref ref-type="aff" rid="aff1001">
            <sup>1</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0009-0005-9233-4888</contrib-id>
          <name>
            <surname>Lopes</surname>
            <given-names>Ana Clara Abdo</given-names>
          </name>
          <role>Conception and design of the research</role>
          <role>Writing of the manuscript</role>
          <role>Critical revision of the manuscript for intellectual content</role>
          <xref ref-type="aff" rid="aff1001">
            <sup>1</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0009-0006-5196-4426</contrib-id>
          <name>
            <surname>Souza</surname>
            <given-names>Fernanda Ritt de</given-names>
          </name>
          <role>Conception and design of the research</role>
          <role>Acquisition of data</role>
          <role>Analysis and interpretation of the data</role>
          <role>Writing of the manuscript</role>
          <role>Critical revision of the manuscript for intellectual content</role>
          <xref ref-type="aff" rid="aff1001">
            <sup>1</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0009-0000-5872-3867</contrib-id>
          <name>
            <surname>Dieter</surname>
            <given-names>Gustavo Ferrucio</given-names>
          </name>
          <role>Conception and design of the research</role>
          <role>Acquisition of data</role>
          <role>Analysis and interpretation of the data</role>
          <role>Writing of the manuscript</role>
          <role>Critical revision of the manuscript for intellectual content</role>
          <xref ref-type="aff" rid="aff1001">
            <sup>1</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0009-0003-2757-7862</contrib-id>
          <name>
            <surname>Nadolny</surname>
            <given-names>Laura</given-names>
          </name>
          <role>Conception and design of the research</role>
          <role>Analysis and interpretation of the data</role>
          <role>Writing of the manuscript</role>
          <role>Critical revision of the manuscript for intellectual content</role>
          <xref ref-type="aff" rid="aff1001">
            <sup>1</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0009-0000-6644-6177</contrib-id>
          <name>
            <surname>Dias</surname>
            <given-names>Leonardo Moreira</given-names>
          </name>
          <role>Conception and design of the research</role>
          <role>Acquisition of data</role>
          <role>Analysis and interpretation of the data</role>
          <role>Statistical analysis</role>
          <role>Critical revision of the manuscript for intellectual content</role>
          <xref ref-type="aff" rid="aff1001">
            <sup>1</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0009-0000-1954-3994</contrib-id>
          <name>
            <surname>Dias</surname>
            <given-names>Let&#237;cia Moreira</given-names>
          </name>
          <role>Conception and design of the research</role>
          <role>Acquisition of data</role>
          <role>Analysis and interpretation of the data</role>
          <role>Writing of the manuscript</role>
          <role>Critical revision of the manuscript for intellectual content</role>
          <xref ref-type="aff" rid="aff2001">
            <sup>2</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0009-0003-6980-2159</contrib-id>
          <name>
            <surname>Farias</surname>
            <given-names>Luis Gustavo Machado</given-names>
          </name>
          <role>Conception and design of the research</role>
          <role>Acquisition of data</role>
          <role>Analysis and interpretation of the data</role>
          <role>Statistical analysis</role>
          <role>Writing of the manuscript</role>
          <xref ref-type="aff" rid="aff1001">
            <sup>1</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0000-0002-2114-9184</contrib-id>
          <name>
            <surname>Basso</surname>
            <given-names>Roberto Oliveira</given-names>
          </name>
          <role>Conception and design of the research</role>
          <role>Acquisition of data</role>
          <role>Analysis and interpretation of the data</role>
          <role>Writing of the manuscript</role>
          <role>Critical revision of the manuscript for intellectual content</role>
          <xref ref-type="aff" rid="aff1001">
            <sup>1</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0000-0001-9872-9120</contrib-id>
          <name>
            <surname>Okimura</surname>
            <given-names>Vit&#243;ria Naomi</given-names>
          </name>
          <role>Conception and design of the research</role>
          <role>Acquisition of data</role>
          <role>Analysis and interpretation of the data</role>
          <role>Writing of the manuscript</role>
          <xref ref-type="aff" rid="aff1001">
            <sup>1</sup>
          </xref>
        </contrib>
      </contrib-group>
      <aff id="aff1001">
        <label>1</label>
        <country country="BR">Brazil</country>
        <institution content-type="original">Faculdade Evang&#233;lica Mackenzie do Paran&#225;, Curitiba, PR &#8211; Brazil</institution>
      </aff>
      <aff id="aff2001">
        <label>2</label>
        <country country="BR">Brazil</country>
        <institution content-type="original">Faculdade Pequeno Pr&#237;ncipe, Curitiba, PR &#8211; Brazil</institution>
      </aff>
      <author-notes>
        <corresp id="c01001"><label>Mailing Address:</label> Ana Clara Abdo Lopes &#8226; Faculdade Evang&#233;lica Mackenzie do Paran&#225; - Rua Padre Anchieta, 2770. Postal Code 80730-000, Curitiba, PR &#8211; Brazil. E-mail: abdolopesanaclara@gmail.com </corresp>
        <fn fn-type="coi-statement">
          <label>Potential Conflict of Interest</label>
          <p>No potential conflict of interest relevant to this article was reported.</p>
        </fn>
        <fn fn-type="edited-by">
          <label>Editor responsible for the review:</label>
          <p> Nadine Clausel</p>
        </fn>
      </author-notes>
      <abstract>
        <title>Abstract</title>
        <sec>
          <title>Background</title>
          <p> Brazil presents regional variations in mortality from cardiomyopathies, influenced by demographic characteristics, socioeconomic inequalities, and differences in access to health services. Between 2001 and 2021, 272,448 deaths from the disease were recorded in the country.</p>
        </sec>
        <sec>
          <title>Objective</title>
          <p> To analyze, at the national level, the epidemiological profile and the spatiotemporal distribution of mortality from cardiomyopathies in Brazil, from 2001 to 2021.</p>
        </sec>
        <sec>
          <title>Methods</title>
          <p> Epidemiological study based on secondary data from the Mortality Information System (SIM/DATASUS) and population estimates from IBGE. Crude mortality rates were calculated per municipality. Temporal analysis was performed using Joinpoint, estimating the annual percentage change (APC). Spatial analysis used GeoDa, with Bayesian smoothing and evaluation of spatial autocorrelation using the Global Moran&#8217;s I and LISA indices. Statistical significance was set at p &lt; 0.05.</p>
        </sec>
        <sec>
          <title>Results</title>
          <p> There were 272,448 deaths due to cardiomyopathies, predominantly in men (58.65%), individuals aged 80 or older (25.74%), white individuals (52.18%), married individuals (36.77%), and those with 1 to 3 years of completed schooling (21.91%). The average mortality rate was 6.66 per 100,000 inhabitants, reaching 7.64 per 100,000 in 2004 and falling to 4.15 per 100,000 in 2020. A significant annual reduction of 1.86% was observed (p &lt; 0.05). Spatial analysis revealed clusters of high mortality, mainly in the Midwest, South, and Southeast regions, with Corumb&#225; de Goi&#225;s standing out (49.34/100,000).</p>
        </sec>
        <sec>
          <title>Conclusion</title>
          <p> There was a significant reduction in mortality from cardiomyopathies, despite the persistence of critical areas that require targeted strategies for surveillance, early diagnosis, and appropriate management.</p>
        </sec>
      </abstract>
      <abstract abstract-type="graphical">
        <p>
          <fig id="f01001">
            <label>Central Illustration</label>
            <caption>
              <title>: Spatiotemporal Analysis of Mortality from Cardiomyopathies in Brazil between 2001 and 2021</title>
            </caption>
            <graphic xlink:href="1678-4170-abc-123-06-e20260064-60064-gf01-en.tif"/></fig>
        </p>
      </abstract>
      <kwd-group xml:lang="en">
        <title>Keywords:</title>
        <kwd>Cardiomyopathies</kwd>
        <kwd>Mortality</kwd>
        <kwd>Spatio-Temporal Analysis</kwd>
        <kwd>Epidemiology</kwd>
      </kwd-group>
    </front-stub>
    <body>
      <sec sec-type="intro">
        <title>Introduction</title>
        <p>Cardiomyopathies comprise a heterogeneous group of myocardial diseases characterized by structural and functional abnormalities that may progress to heart failure, malignant arrhythmias, and sudden cardiac death. According to the International Classification of Diseases (ICD-10), these conditions are classified under code I42. Over recent decades, a global increase in the prevalence of cardiomyopathies has been observed, particularly dilated cardiomyopathy, whose incidence ranges from 5 to 10 cases per 100,000 inhabitants per year. This trend reflects population aging and the growing burden of chronic non-communicable diseases.<sup><xref ref-type="bibr" rid="B1">1</xref>-<xref ref-type="bibr" rid="B3">3</xref></sup></p>
        <p>The etiology of cardiomyopathies is multifactorial and includes inflammatory, toxic, metabolic, and genetic causes, with particular emphasis on hereditary mutations, especially in hypertrophic cardiomyopathy.<sup><xref ref-type="bibr" rid="B3">3</xref>,<xref ref-type="bibr" rid="B4">4</xref></sup> Traditional cardiovascular risk factors such as arterial hypertension, diabetes mellitus, obesity, and excessive alcohol consumption are also associated with the development and progression of these conditions.<sup><xref ref-type="bibr" rid="B5">5</xref></sup></p>
        <p>Clinically, patients may present with dyspnea, fatigue, palpitations, and peripheral edema, with manifestations ranging from asymptomatic forms in early stages to severe heart failure or sudden cardiac death.<sup><xref ref-type="bibr" rid="B6">6</xref>,<xref ref-type="bibr" rid="B7">7</xref></sup> Diagnosis is based on clinical assessment combined with complementary tests, with echocardiography considered the gold standard for evaluating ventricular function and cardiac morphology.<sup><xref ref-type="bibr" rid="B8">8</xref></sup> Treatment is directed toward the underlying etiology and includes both pharmacological and non-pharmacological therapies. Despite therapeutic advances, complications and mortality remain significant.<sup><xref ref-type="bibr" rid="B9">9</xref>,<xref ref-type="bibr" rid="B10">10</xref></sup></p>
        <p>In Brazil, studies have demonstrated marked regional variations in mortality from cardiomyopathies, suggesting the influence of demographic and socioeconomic factors, as well as inequalities in access to health services.<sup><xref ref-type="bibr" rid="B11">11</xref>-<xref ref-type="bibr" rid="B13">13</xref></sup> However, most available investigations are limited by short time frames or isolated regional analyses, hindering an integrated understanding of the spatial distribution and temporal trends of cardiomyopathy-related mortality at the national level. <sup><xref ref-type="bibr" rid="B14">14</xref>,<xref ref-type="bibr" rid="B15">15</xref></sup></p>
        <p>While previous studies often report regional differences using aggregated data, spatiotemporal analysis provides a more comprehensive approach by jointly evaluating variations across space and time. This allows the identification of persistent or emerging clusters and a better understanding of dynamic geographic patterns.<sup><xref ref-type="bibr" rid="B16">16</xref>,<xref ref-type="bibr" rid="B17">17</xref></sup>Additionally, methodological issues such as spatial aggregation (modifiable areal unit problem) and ecological bias may influence results, reinforcing the importance of appropriate analytical strategies in spatial epidemiology.<sup><xref ref-type="bibr" rid="B18">18</xref></sup> Bayesian smoothing techniques and other rate-stabilizing approaches further improve estimate reliability in small areas by reducing random fluctuations.<sup><xref ref-type="bibr" rid="B19">19</xref></sup></p>
        <p>In this context, the present study aimed to analyze the epidemiological profile and the spatiotemporal distribution of mortality from cardiomyopathies in Brazil between 2001 and 2021. Secondary objectives included identifying spatial patterns and mortality clusters, as well as describing the distribution of deaths according to sociodemographic characteristics. Additionally, the place of occurrence of deaths was analyzed as a descriptive variable to complement the epidemiological profile.</p>
      </sec>
      <sec sec-type="methods">
        <title>Methods</title>
        <p>This was an epidemiological study based on the analysis of secondary health data, with a focus on spatiotemporal analysis. Data on deaths due to cardiomyopathies were obtained from the Mortality Information System (SIM) of the Department of Informatics of the Brazilian Unified Health System (DATASUS), Ministry of Health, accessed through the Tabnet platform, considering both place of residence and place of occurrence of death. Population data used as denominators for mortality rate calculations were obtained from DATASUS, based on IBGE population estimates by municipality, sex, and age for the period 2000&#8211;2021. These estimates are derived from demographic projection methods and intercensal adjustments performed by IBGE, using the 2010 national census as a reference to ensure consistency across the time series.</p>
        <p>To analyze mortality trends in Brazil, the period from 2001 to 2021 was selected to allow a comprehensive assessment of the outcome over time. This was a census-based analysis, with no individual selection of participants, including all deaths from cardiomyopathies recorded in the SIM between 2001 and 2021. The population was defined as individuals of all ages. The variables analyzed included sex, age group, race/skin color, marital status, educational level, deaths by place of residence, year of death, place of death, and place of occurrence. Educational level categories followed the classification available in the DATASUS database and were maintained as originally provided to ensure consistency with national health information systems. The race/skin color variable was categorized according to the standardized classification adopted by DATASUS and the Brazilian Institute of Geography and Statistics (IBGE).</p>
        <p>The underlying cause of death was defined according to the ICD-10 code I42. Epidemiological profile data and death frequencies were compiled using Microsoft Excel for Microsoft 365 MSO. Categorical variables were analyzed using absolute and relative frequencies. Annual mortality rates were calculated using the total number of deaths in each state as the numerator and the corresponding state population for each year as the denominator, expressed per 100,000 inhabitants. Municipality-level mortality rates were calculated using a population-averaging approach, defined as the mean number of deaths over the study period divided by the mean population between 2001 and 2021, expressed per 100,000 inhabitants. The mean population was obtained by summing annual population estimates over the study period and dividing by the number of years. This approach was adopted to reduce annual fluctuations and provide more stable estimates for small-area analyses; however, it does not account for differences in age structure across municipalities, which may influence comparisons.</p>
        <p>The primary outcome of the study was mortality from cardiomyopathies, defined according to ICD-10 code I42. Initially, temporal trend analysis of annual mortality rates was performed (<xref ref-type="fig" rid="f02001">Figure 1</xref>). The annual percent change (APC) in mortality trends was estimated with a 95% confidence interval (95% CI), and statistical significance was set at p&lt;0.05. Temporal trend analysis was performed using the Joinpoint Regression Program (version 5.0.2, National Cancer Institute, USA), using weighted least squares regression, estimating the annual percent change (APC) and 95% confidence intervals. A log-linear model was fitted, and the number of joinpoints was determined using permutation tests according to the software&#8217;s default settings. The final model selected included zero joinpoints (i.e., a single temporal trend). Subsequently, spatial analysis of cardiomyopathy mortality distribution was performed for each region of Brazil (<xref ref-type="fig" rid="f03001">Figure 2</xref>).</p>
        <p>
          <fig id="f02001">
            <label>Figure 1</label>
            <caption>
              <title>&#8211; Temporal pattern of mortality from cardiomyopathies in Brazil between 2001 and 2021. *Indicates that the Annual Percent Change (APC) is a significantly different from zero at the alpha = 0,05 level. Final Selected Model: 1 Joinpoint. Source: The authors (2025).</title>
            </caption>
            <graphic xlink:href="1678-4170-abc-123-06-e20260064-60064-gf02-en.tif"/></fig>
        </p>
        <p>
          <fig id="f03001">
            <label>Figure 2</label>
            <caption>
              <title>&#8211; Crude mortality rate from cardiomyopathies in Brazil, 2001&#8211;2021. Source: The authors (2025).</title>
            </caption>
            <graphic xlink:href="1678-4170-abc-123-06-e20260064-60064-gf03-en.tif"/></fig>
        </p>
        <p>Spatial smoothing was performed using the local empirical Bayesian method to reduce random fluctuations in municipal mortality rates. A spatial weights matrix was constructed based on first-order queen contiguity, in which municipalities sharing at least one common boundary or vertex were considered neighbors.</p>
        <p>Municipal boundaries were defined according to the official territorial division provided by the Brazilian Institute of Geography and Statistics (IBGE), without any modifications. Mortality data from DATASUS were linked to municipalities using official IBGE codes.</p>
        <p>Sensitivity analyses using alternative neighborhood definitions (e.g., rook contiguity) were not performed. However, the use of queen contiguity was chosen due to its broader neighborhood structure, which is widely used in epidemiological spatial studies and provides more stable estimates in areas with heterogeneous territorial configurations.</p>
        <p>Spatial analyses and thematic map construction were performed using GeoDa software, version 1.22.0.4 (2023). The categorization of map legends followed different approaches according to the type of analysis performed. For crude mortality rates (<xref ref-type="fig" rid="f02001">Figures 1</xref> and <xref ref-type="fig" rid="f03001">2</xref>), values were classified using quantiles, allowing a balanced distribution of municipalities across categories, and improving visualization and comparability. For the smoothed rates obtained through the local empirical Bayesian method (<xref ref-type="fig" rid="f04001">Figures 3</xref>&#8211;<xref ref-type="fig" rid="f06001">5</xref>), classification reflected the distribution of adjusted values, aiming to highlight spatial patterns after reducing random fluctuations. These classification strategies are commonly used in exploratory spatial analyses; however, different categorization methods may lead to variations in the visual interpretation of spatial patterns.</p>
        <p>
          <fig id="f04001">
            <label>Figure 3</label>
            <caption>
              <title>&#8211; Mortality rate from cardiomyopathies smoothed using the local empirical Bayesian method in Brazil, 2001&#8211;2021. Source: The authors (2025).</title>
            </caption>
            <graphic xlink:href="1678-4170-abc-123-06-e20260064-60064-gf04-en.tif"/></fig>
        </p>
        <p>
          <fig id="f06001">
            <label>Figure 5</label>
            <caption>
              <title>&#8211; Statistical significance of spatial clusters. Source: The authors (2025).</title>
            </caption>
            <graphic xlink:href="1678-4170-abc-123-06-e20260064-60064-gf05-en.tif"/></fig>
        </p>
        <p>Spatial clusters were identified using the Global and Local Moran&#8217;s I indices. Global Moran&#8217;s I was used to test the hypothesis of spatial dependence by measuring the correlation among first-order neighbors, with values ranging from &#8722;1 to +1; values close to zero indicate the absence of spatial autocorrelation, with statistical significance defined as p&lt;0.05. When spatial dependence was confirmed, Local Indicators of Spatial Association (LISA) were applied to identify local spatial clusters, also considering p&lt;0.05. The results of these analyses were presented using Moran and LISA maps (<xref ref-type="fig" rid="f05001">Figures 4</xref> and <xref ref-type="fig" rid="f06001">5</xref>). The Moran map graphically represents the degree of similarity between neighboring areas and is divided into four quadrants:</p>
        <p>
          <fig id="f05001">
            <label>Figure 4</label>
            <caption>
              <title>&#8211; Spatial clusters of mortality from cardiomyopathies in Brazil, 2001&#8211;2021. Source: The authors 2025).</title>
            </caption>
            <graphic xlink:href="1678-4170-abc-123-06-e20260064-60064-gf06-en.tif"/></fig>
        </p>
        <list list-type="order">
          <list-item>
            <p>High&#8211;high (right superior quadrant): municipalities with high mortality rates surrounded by areas with similarly high rates</p>
          </list-item>
          <list-item>
            <p>Low&#8211;low (left inferior quadrant): municipalities with low mortality rates surrounded by areas with low rates</p>
          </list-item>
          <list-item>
            <p>High&#8211;low (right inferior quadrant): municipalities with high rates surrounded by low-rate neighbors</p>
          </list-item>
          <list-item>
            <p>Low&#8211;high (left superior quadrant): municipalities with low rates surrounded by high-rate neighbors</p>
          </list-item>
        </list>
        <p>The data used in this study are publicly available, aggregated, and do not allow individual identification; therefore, there is no potential for physical or moral harm to individuals or communities. Accordingly, the study was exempt from approval by a Research Ethics Committee and is in accordance with Resolution No. 466/2012 of the Brazilian National Health Council. Additionally, as the study is based on secondary data, the findings may reflect limitations related to data quality, such as underreporting and incomplete information in death certificates.</p>
      </sec>
      <sec sec-type="results">
        <title>Results</title>
        <sec>
          <title>Epidemiological profile</title>
          <p>Between 2001 and 2021, a total of 272,448 deaths due to cardiomyopathies were recorded in Brazil. As shown in the <xref ref-type="fig" rid="f01001">Central figure</xref>, the sociodemographic characterization revealed a higher occurrence of deaths among male individuals and those aged 80 years or older. There was also a predominance of deaths among individuals classified as White, married, and with low educational attainment (1-3 years of schooling). The detailed epidemiological profile of deaths is presented in <xref ref-type="table" rid="t1001">Table 1</xref>.</p>
          <p>
            <table-wrap id="t1001">
              <label>Table 1</label>
              <caption>
                <title>&#8211; Epidemiological profile of individuals who died from cardiomyopathies in Brazil between 2001 and 2021</title>
              </caption>
              <table frame="hsides" rules="groups">
                <colgroup width="33%">
                  <col/>
                  <col/>
                  <col/>
                </colgroup>
                <thead>
                  <tr>
                    <th align="left">Variable</th>
                    <th>n</th>
                    <th>%</th>
                  </tr>
                </thead>
                <tbody>
                  <tr>
                    <td colspan="3">
                      <bold>Sex</bold>
                    </td>
                  </tr>
                  <tr>
                    <td>Male</td>
                    <td align="center">159,782</td>
                    <td align="center">58.65</td>
                  </tr>
                  <tr>
                    <td>Female</td>
                    <td align="center">112,618</td>
                    <td align="center">41.33</td>
                  </tr>
                  <tr>
                    <td>Missing</td>
                    <td align="center">48</td>
                    <td align="center">0.02</td>
                  </tr>
                  <tr>
                    <td colspan="3">
                      <bold>Age group (years)</bold>
                    </td>
                  </tr>
                  <tr>
                    <td>&lt;1</td>
                    <td align="center">2,611</td>
                    <td align="center">0.96</td>
                  </tr>
                  <tr>
                    <td>1-4</td>
                    <td align="center">1,327</td>
                    <td align="center">0.49</td>
                  </tr>
                  <tr>
                    <td>5-9</td>
                    <td align="center">572</td>
                    <td align="center">0.21</td>
                  </tr>
                  <tr>
                    <td>10-14</td>
                    <td align="center">834</td>
                    <td align="center">0.31</td>
                  </tr>
                  <tr>
                    <td>15-19</td>
                    <td align="center">1,664</td>
                    <td align="center">0.61</td>
                  </tr>
                  <tr>
                    <td>20-29</td>
                    <td align="center">5,635</td>
                    <td align="center">2.07</td>
                  </tr>
                  <tr>
                    <td>30-39</td>
                    <td align="center">12,571</td>
                    <td align="center">4.61</td>
                  </tr>
                  <tr>
                    <td>40-49</td>
                    <td align="center">24,747</td>
                    <td align="center">9.08</td>
                  </tr>
                  <tr>
                    <td>50-59</td>
                    <td align="center">39,897</td>
                    <td align="center">14.64</td>
                  </tr>
                  <tr>
                    <td>60-69</td>
                    <td align="center">51,505</td>
                    <td align="center">18.90</td>
                  </tr>
                  <tr>
                    <td>70-79</td>
                    <td align="center">59,978</td>
                    <td align="center">22.01</td>
                  </tr>
                  <tr>
                    <td>&#8805;80</td>
                    <td align="center">70,132</td>
                    <td align="center">25.75</td>
                  </tr>
                  <tr>
                    <td>Age missing</td>
                    <td align="center">975</td>
                    <td align="center">0.36</td>
                  </tr>
                  <tr>
                    <td colspan="3">
                      <bold>Race/ethnicity</bold>
                    </td>
                  </tr>
                  <tr>
                    <td>White</td>
                    <td align="center">142,166</td>
                    <td align="center">52.18</td>
                  </tr>
                  <tr>
                    <td>Black</td>
                    <td align="center">25,990</td>
                    <td align="center">9.54</td>
                  </tr>
                  <tr>
                    <td>Brown</td>
                    <td align="center">86,935</td>
                    <td align="center">31.91</td>
                  </tr>
                  <tr>
                    <td>Asian</td>
                    <td align="center">1,341</td>
                    <td align="center">0.49</td>
                  </tr>
                  <tr>
                    <td>Indigenous</td>
                    <td align="center">380</td>
                    <td align="center">0.14</td>
                  </tr>
                  <tr>
                    <td>Missing</td>
                    <td align="center">15,636</td>
                    <td align="center">5.74</td>
                  </tr>
                  <tr>
                    <td colspan="3">
                      <bold>Marital status</bold>
                    </td>
                  </tr>
                  <tr>
                    <td>Single</td>
                    <td align="center">66,998</td>
                    <td align="center">24.59</td>
                  </tr>
                  <tr>
                    <td>Married</td>
                    <td align="center">100,189</td>
                    <td align="center">36.77</td>
                  </tr>
                  <tr>
                    <td>Widowed</td>
                    <td align="center">66,280</td>
                    <td align="center">24.33</td>
                  </tr>
                  <tr>
                    <td>Legally separated</td>
                    <td align="center">15,681</td>
                    <td align="center">5.76</td>
                  </tr>
                  <tr>
                    <td>Other</td>
                    <td align="center">3,344</td>
                    <td align="center">1.23</td>
                  </tr>
                  <tr>
                    <td>Missing</td>
                    <td align="center">19,956</td>
                    <td align="center">7.32</td>
                  </tr>
                  <tr>
                    <td colspan="3">
                      <bold>Education (years)</bold>
                    </td>
                  </tr>
                  <tr>
                    <td>None</td>
                    <td align="center">39,515</td>
                    <td align="center">14.50</td>
                  </tr>
                  <tr>
                    <td>1-3</td>
                    <td align="center">59,684</td>
                    <td align="center">21.91</td>
                  </tr>
                  <tr>
                    <td>4-7</td>
                    <td align="center">56,125</td>
                    <td align="center">20.60</td>
                  </tr>
                  <tr>
                    <td>8-11</td>
                    <td align="center">29,699</td>
                    <td align="center">10.90</td>
                  </tr>
                  <tr>
                    <td>&#8805;12</td>
                    <td align="center">10,933</td>
                    <td align="center">4.01</td>
                  </tr>
                  <tr>
                    <td>Missing</td>
                    <td align="center">76,492</td>
                    <td align="center">28.08</td>
                  </tr>
                  <tr>
                    <td colspan="3">
                      <bold>Place of death</bold>
                    </td>
                  </tr>
                  <tr>
                    <td>Hospital</td>
                    <td align="center">168,444</td>
                    <td align="center">61.83</td>
                  </tr>
                  <tr>
                    <td>Other health facility</td>
                    <td align="center">16,890</td>
                    <td align="center">6.20</td>
                  </tr>
                  <tr>
                    <td>Home</td>
                    <td align="center">72,962</td>
                    <td align="center">26.78</td>
                  </tr>
                  <tr>
                    <td>Public place</td>
                    <td align="center">4,852</td>
                    <td align="center">1.78</td>
                  </tr>
                  <tr>
                    <td>Others</td>
                    <td align="center">8,948</td>
                    <td align="center">3.28</td>
                  </tr>
                  <tr>
                    <td>Missing</td>
                    <td align="center">352</td>
                    <td align="center">0.13</td>
                  </tr>
                </tbody>
              </table>
              <table-wrap-foot>
                <attrib>
                  <italic>Source: Department of Informatics of the Unified Health System (DATASUS), Ministry of Health, Brazil.</italic>
                </attrib>
              </table-wrap-foot>
            </table-wrap>
          </p>
          <p>Regarding the place of occurrence, most deaths occurred in hospital settings, followed by deaths at home, while the remaining categories showed lower relative frequencies (<xref ref-type="table" rid="t1001">Table 1</xref>).</p>
        </sec>
        <sec>
          <title>Temporal analysis of mortality</title>
          <p>The mean mortality rate from cardiomyopathies during the study period was 6.66 deaths per 100,000 inhabitants, with the highest rate observed in 2004 and the lowest in 2020. Temporal trend analysis demonstrated a significant reduction in mortality over the study period, with an average annual decrease of 1.86% (APC = &#8722;1.86%; 95% CI [&#8722;2.73; &#8722;1.03], p &lt; 0.001). The final model did not include joinpoints, indicating a constant declining trend over time, as shown in <xref ref-type="fig" rid="f01001">Central Figure</xref> and <xref ref-type="fig" rid="f02001">Figure 1</xref>.</p>
        </sec>
        <sec>
          <title>Spatial analysis of mortality</title>
          <p>Spatial analysis of crude mortality rates revealed marked heterogeneity in the distribution of deaths from cardiomyopathies across Brazil, with higher rates predominantly concentrated in the Midwest, South, and Southeast regions (<xref ref-type="fig" rid="f03001">Figure 2</xref>). Municipalities with the highest mortality rates during the period included Corumb&#225; de Goi&#225;s, S&#227;o Jos&#233; do Rio Pardo, Itajobi, Santo In&#225;cio, and S&#227;o Manuel.</p>
          <p>After applying local empirical Bayesian smoothing, a clearer spatial pattern emerged, with clustering of municipalities presenting elevated mortality rates mainly in the Midwest and Southeast regions of Brazil (<xref ref-type="fig" rid="f04001">Figure 3</xref>). Spatial autocorrelation analysis confirmed the presence of significant spatial dependence, as evidenced by a positive Global Moran&#8217;s I (I = 0.864; p = 0.01).</p>
          <p>Local spatial autocorrelation analysis (LISA) identified statistically significant spatial clusters. High&#8211;high clusters were mainly observed in the Midwest, South, and Southeast regions, whereas low&#8211;low clusters were predominantly located in the North and Northeast regions of the country (<xref ref-type="fig" rid="f01001">Central Figure</xref>, <xref ref-type="fig" rid="f05001">Figures 4</xref> and <xref ref-type="fig" rid="f06001">5</xref>).</p>
        </sec>
      </sec>
      <sec sec-type="discussion">
        <title>Discussion</title>
        <p>The findings of this study demonstrate that, between 2001 and 2021, a total of 272,448 deaths due to cardiomyopathies were recorded in Brazil, predominantly among men (58.65%), individuals aged 80 years or older (25.74%), and those self-identified as White (52.18%). This epidemiological profile is consistent with data previously reported in the literature, which indicate greater severity and lethality of cardiomyopathies in older populations and in males, associated with cardiovascular aging and a higher burden of comorbidities, such as systemic arterial hypertension and diabetes mellitus.<sup><xref ref-type="bibr" rid="B2">2</xref>,<xref ref-type="bibr" rid="B5">5</xref>,<xref ref-type="bibr" rid="B20">20</xref></sup></p>
        <p>The higher mortality observed among men may be partly explained by biological and hormonal differences, as well as greater exposure to risk factors and a higher propensity for structural myocardial alterations and cardiovascular events, such as sudden cardiac death, which is consistent with findings from international studies and clinical guidelines.<sup><xref ref-type="bibr" rid="B2">2</xref>,<xref ref-type="bibr" rid="B6">6</xref>,<xref ref-type="bibr" rid="B7">7</xref></sup> Furthermore, concerning age group, population aging is one of the main risk factors for the development of this condition, particularly in the case of dilated cardiomyopathy, due to increased myocardial vulnerability and the higher prevalence of the aforementioned comorbidities. This reinforces the relevance of this disease in countries undergoing advanced demographic transition, such as Brazil.<sup><xref ref-type="bibr" rid="B2">2</xref>,<xref ref-type="bibr" rid="B5">5</xref>,<xref ref-type="bibr" rid="B7">7</xref>,<xref ref-type="bibr" rid="B20">20</xref></sup></p>
        <p>Lower educational attainment was associated with higher mortality from cardiomyopathies: approximately 21.91% of individuals who died from this condition had 1 to 3 years of schooling. This finding is in line with that reported by Santos et al.,<sup><xref ref-type="bibr" rid="B14">14</xref></sup> who suggest the influence of social determinants of health on cardiovascular outcomes, including reduced access to specialized healthcare services, delayed diagnosis, and lower therapeutic adherence. Additionally, the predominance of deaths among married individuals (36.77%) may reflect the greater longevity observed in this group, a phenomenon previously described in Brazilian demographic analyses.<sup><xref ref-type="bibr" rid="B21">21</xref>,<xref ref-type="bibr" rid="B22">22</xref></sup></p>
        <p>Regarding the place of death, most deaths occurred in the hospital setting (61.83%), which may indicate not only increased access to hospital admissions but also late diagnosis of cardiomyopathies, given that these conditions often go unnoticed in their early stages.<sup><xref ref-type="bibr" rid="B6">6</xref></sup> Conversely, the significant proportion of deaths occurring at home (26.78%) suggests weaknesses in outpatient follow-up and in the early management of the disease, particularly in regions with major geographic and social constraints, as previously highlighted by Ziaeian and Fonarow.<sup><xref ref-type="bibr" rid="B2">2</xref></sup></p>
        <p>The observed reduction in mortality from cardiomyopathies (average annual decrease of 1.86%; p &lt; 0.05) may be partially related to therapeutic advances in recent decades, including the use of angiotensin-converting enzyme inhibitors, beta-blockers, implantable devices, and heart transplantation. However, access to these therapies remains heterogeneous across Brazil, and their impact on population-level mortality likely varies according to regional healthcare infrastructure and availability of specialized services.<sup><xref ref-type="bibr" rid="B9">9</xref>,<xref ref-type="bibr" rid="B10">10</xref></sup> Current clinical guidelines emphasize that early diagnosis and multidisciplinary treatment are essential to reduce mortality.<sup><xref ref-type="bibr" rid="B9">9</xref></sup> Nevertheless, the persistence of high mortality levels suggests that the benefits of these advances are not evenly distributed across the national territory. The peak observed in 2004 may reflect improvements in case detection and reporting during the early consolidation of the Mortality Information System (SIM), as well as temporal fluctuations inherent to population-based analyses.<sup><xref ref-type="bibr" rid="B23">23</xref>,<xref ref-type="bibr" rid="B24">24</xref></sup></p>
        <p>Notably, the reduction in mortality observed in 2020, followed by a partial increase in 2021, should be interpreted in the context of the COVID-19 pandemic. This period was marked by disruptions in healthcare access, delays in diagnosis, and potential misclassification of causes of death, particularly for cardiovascular conditions. Previous studies in Brazil have demonstrated that excess mortality during the pandemic may be associated with underreporting, misdiagnosis, and reduced access to care for non-COVID conditions.<sup><xref ref-type="bibr" rid="B25">25</xref></sup></p>
        <p>In this context, spatial analysis revealed significant differences among the regions of the country. High-mortality clusters were observed mainly in the Central-West, South, and Southeast regions, with emphasis on municipalities such as Corumb&#225; de Goi&#225;s and S&#227;o Jos&#233; do Rio Pardo. This scenario suggests the influence of demographic factors, such as a higher proportion of elderly populations in these areas, combined with a greater prevalence of cardiovascular risk factors. Additionally, greater diagnostic capacity and more robust case reporting in these regions may contribute to the higher observed rates. The high value of Global Moran&#8217;s I observed in this study may reflect the use of smoothed rates and the strong regional clustering pattern in Brazil. Additionally, the use of population-averaged rates may have contributed to increased spatial stability and autocorrelation. It is also important to consider that extreme values observed in specific municipalities may be influenced by small population sizes, leading to rate instability, as well as by local healthcare structures or reporting patterns.<sup><xref ref-type="bibr" rid="B11">11</xref></sup></p>
        <p>In contrast, in the North and Northeast regions, where clusters of low mortality were identified, it is plausible that these findings do not necessarily reflect a lower occurrence of the disease, but rather underdiagnosis and/or underreporting. In these regions, access to diagnostic tests such as echocardiography (essential for the assessment of myocardial function) remains limited, and death notification systems present significant shortcomings.<sup><xref ref-type="bibr" rid="B7">7</xref>,<xref ref-type="bibr" rid="B14">14</xref></sup> In this regard, a study conducted in the state of Cear&#225; demonstrated that municipalities with smaller populations and less healthcare infrastructure tend to present worse outcomes for cardiovascular diseases, which supports this interpretation.<sup><xref ref-type="bibr" rid="B14">14</xref></sup> These findings underscore the need for caution in interpreting mortality rates and highlight relevant regional inequalities.</p>
        <p>Such regional disparities reinforce the influence of social determinants of health and weaknesses in the healthcare system for chronic disease management on patient outcomes.<sup><xref ref-type="bibr" rid="B26">26</xref></sup> In this context, the role of Primary Health Care (PHC) in surveillance and early screening of cardiomyopathies is particularly noteworthy.<sup><xref ref-type="bibr" rid="B27">27</xref></sup> Therefore, expanding access to diagnostic tests, such as echocardiography, and investing in the training of public healthcare professionals are strategies recommended by current guidelines and potentially relevant for improving outcomes at the national level.<sup><xref ref-type="bibr" rid="B9">9</xref></sup></p>
        <p>Although the present study did not directly evaluate specific socioeconomic indicators, such as income inequality (e.g., Gini coefficient), Human Development Index (HDI), healthcare expenditure, or primary healthcare coverage, these factors may partially explain the regional disparities observed. Previous studies in Brazil have demonstrated that areas with lower socioeconomic development and reduced access to healthcare services tend to present worse cardiovascular outcomes, including higher mortality rates.<sup><xref ref-type="bibr" rid="B1">1</xref>-<xref ref-type="bibr" rid="B3">3</xref></sup> In particular, differences in primary health care coverage and access to specialized diagnostic resources may contribute to delays in diagnosis and treatment, influencing mortality patterns across regions.<sup><xref ref-type="bibr" rid="B28">28</xref>-<xref ref-type="bibr" rid="B30">30</xref></sup></p>
        <p>Another important consideration is the clinical and etiological heterogeneity encompassed by ICD-10 code I42, which includes distinct cardiomyopathy subtypes, such as dilated, hypertrophic, restrictive, and secondary forms, with different pathophysiological mechanisms and prognoses.<sup><xref ref-type="bibr" rid="B3">3</xref></sup> When analyzed in an aggregated manner, this heterogeneity may obscure subtype-specific epidemiological patterns and lead to oversimplified interpretations of mortality trends.</p>
        <p>Additionally, underdiagnosis and underreporting should be considered, particularly in regions with limited access to specialized diagnostic tools, such as echocardiography and cardiac magnetic resonance imaging, as well as potential limitations in the quality of death certificate completion. Regional disparities in access to cardiological services and variability in health information systems may contribute to misclassification and a higher proportion of ill-defined causes of death.<sup><xref ref-type="bibr" rid="B31">31</xref></sup></p>
        <p>Furthermore, cardiomyopathy-related deaths may be underestimated due to the frequent recording of heart failure (ICD-10 code I50) as the underlying cause of death, even when resulting from cardiomyopathies (ICD-10 code I42). This limitation is reinforced by the inability of the DATASUS platform to identify or cross-reference associated causes of death.<sup><xref ref-type="bibr" rid="B31">31</xref></sup></p>
        <p>Chagas cardiomyopathy, a relevant cause of myocardial disease in Brazil, is classified under a different ICD-10 code (B57.2) and may not be fully captured in analyses restricted to code I42.<sup><xref ref-type="bibr" rid="B32">32</xref></sup> Consequently, areas identified with lower mortality rates may reflect limitations in diagnostic capacity and data quality rather than a truly reduced disease burden, requiring cautious interpretation in spatiotemporal analyses.<sup><xref ref-type="bibr" rid="B31">31</xref></sup></p>
        <p>The use of secondary data represents a limitation of this study and may introduce biases related to underreporting, incomplete information, and inconsistencies in the recording of causes of death.<sup><xref ref-type="bibr" rid="B32">32</xref>-<xref ref-type="bibr" rid="B34">34</xref></sup> Analyses based on population estimates may be affected by the time lag of the most recent demographic census (2010), potentially influencing the accuracy of mortality rate estimates.<sup><xref ref-type="bibr" rid="B34">34</xref></sup> As an ecological study, the findings are subject to aggregation bias and may not reflect individual-level associations.<sup><xref ref-type="bibr" rid="B35">35</xref></sup> Furthermore, the analysis considered only the underlying cause of death, which may underestimate cardiomyopathy-related mortality when these conditions are recorded as associated causes.<sup><xref ref-type="bibr" rid="B36">36</xref></sup> The absence of age standardization may also limit comparisons between regions, given the strong age dependence of cardiomyopathy mortality and regional differences in population age structure.<sup><xref ref-type="bibr" rid="B37">37</xref></sup> Finally, the use of averaged mortality rates over a long period may obscure temporal variations and demographic changes, potentially influencing the interpretation of spatial patterns.<sup><xref ref-type="bibr" rid="B34">34</xref></sup></p>
        <p>An additional limitation relates to the potential for type I error due to multiple comparisons in spatial analyses involving a large number of municipalities. Statistical significance was set at p&lt;0.05 without formal adjustment for multiple testing, such as False Discovery Rate (FDR) correction. Although this approach may increase the likelihood of identifying spurious clusters, it is commonly adopted in exploratory spatial analyses.<sup><xref ref-type="bibr" rid="B38">38</xref></sup> Therefore, the identified spatial patterns should be interpreted with caution.</p>
        <p>In summary, the spatiotemporal analysis identified areas of the Brazilian territory with high mortality rates from cardiomyopathies, highlighting significant regional inequalities. Accordingly, the findings of the present study suggest the need for strategies aligned with local realities and specificities, aimed at strengthening surveillance and care for cardiomyopathies. In this context, the results provide scientific evidence to support the planning and organization of health actions, with an emphasis on Primary Health Care (PHC), particularly in areas of greater vulnerability, intending to expand access to care and, consequently, promote health and contribute to the reduction of morbidity and mortality from cardiomyopathies in Brazil.</p>
      </sec>
      <sec sec-type="conclusions">
        <title>Conclusion</title>
        <p>Between 2001 and 2021, mortality from cardiomyopathies in Brazil showed a significant temporal decline, with an average annual reduction of 1.86%, although it remained substantial throughout the study period. A higher frequency of deaths was observed among men, individuals aged 80 years or older, those who self-identified as White, married individuals, and those with lower levels of education. Spatial analysis revealed positive spatial autocorrelation and identified clusters of high mortality predominantly in the Central-West, South, and Southeast regions, while clusters of low mortality were mainly observed in the North and Northeast regions. These findings confirm the regional heterogeneity of cardiomyopathy-related mortality in the country and demonstrate the usefulness of spatiotemporal analysis for identifying priority areas in public health.</p>
      </sec>
    </body>
    <back>
      <fn-group>
        <fn fn-type="other">
          <label>Study Association</label>
          <p> This study is not associated with any thesis or dissertation work.</p>
        </fn>
        <fn fn-type="other">
          <label>Ethics Approval and Consent to Participate</label>
          <p> This article does not contain any studies with human participants or animals performed by any of the authors.</p>
        </fn>
        <fn fn-type="other">
          <label>Use of Artificial Intelligence</label>
          <p> During the preparation of this work, the author(s) used Deepl Translate is a neural network-based machine translation system, widely recognized for its quality of linguistic output. After using this tool/service, the author(s) reviewed and edited the content as needed and take full responsibility for the content of the published article.</p>
        </fn>
        <fn fn-type="data-availability" specific-use="data-available">
          <label>Availability of Research Data</label>
          <p> The content is available at the link [<ext-link ext-link-type="uri" xlink:href="http://tabnet.datasus.gov.br/">http://tabnet.datasus.gov.br/</ext-link>]. Tabnet _ DATASUS. Departamento de Inform&#225;tica do Sistema &#218;nico de Sa&#250;de.</p>
        </fn>
        <fn fn-type="financial-disclosure">
          <label>Sources of Funding</label>
          <p> There were no external funding sources for this study.</p>
        </fn>
      </fn-group>
    </back>
  </sub-article>
</article>
